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SIAM Conference on Uncertainty Quantification (UQ20)

Tuesday – 24.03.2020

07:30

iCal
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Room :
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MW Magistrale/Foyer

Registration - If too busy, come back any time later during the conference!

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Duration :
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45 Minutes

08:30

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Frances Kuo | University of New South Wales | Australia

IP01: Frances Y. Kuo: New Quasi-Monte Carlo Strategies for UQ

Room:
MW HS 2001

Topic:
High-dimensional approximation

Form of presentation:
Plenary Lecture

Duration:
45 Minutes

08:30

New Quasi-Monte Carlo Strategies for UQ

08:30

iCal

IP01 - streamed from HS 2001: Frances Y. Kuo: New Quasi-Monte Carlo Strategies for UQ

Room:
MW HS 0001

Topic:
High-dimensional approximation

Form of presentation:
Plenary Lecture

Duration:
45 Minutes

09:15

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George Em Karniadakis | Brown University | United States

IP02: George Em Karniadakis: Physics-Informed Neural Networks (PINNs) with Uncertainty Quantification

Room:
MW HS 2001

Topic:
Physics-informed and data-driven UQ methods

Form of presentation:
Plenary Lecture

Duration:
45 Minutes

09:15

Physics-Informed Neural Networks (PINNs) with Uncertainty Quantification

09:15

iCal

IP02 - streamed from HS 2001: George Em Karniadakis: Physics-Informed Neural Networks (PINNs) with Uncertainty Quantification

Room:
MW HS 0001

Topic:
Physics-informed and data-driven UQ methods

Form of presentation:
Plenary Lecture

Duration:
45 Minutes

10:30

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Tijana Janjic Pfander | University of Munich | Germany

MT01: Tijana Janjic Pfander: Data assimilation and ensemble forecasting in meteorology

Room:
MW HS 2001

Topic:
Data assimilation

Form of presentation:
Mini-tutorial

Duration:
120 Minutes

10:30

Data assimilation and ensemble forecasting in meteorology

10:30

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Garry Maskaly | Los Alamos National Laboratory | United States

Francois Hemez | Lawrence Livermore National Laboratory | United States

Jessica Pillow | The University of Arizona | United States

Jim Gaffney | Lawrence Livermore National Laboratory | United States

MS531: Physics Interpretation-Based Uncertainty Quantification

Room:
MW HS 0001

Topic:
Physics-informed and data-driven UQ methods

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Data collection has always been plagued by the presence of both errors in acquisition and various sources of uncertainty. Given significant growth in the field of uncertainty quantification (UQ), it is imperative for the data analysis to provide errors and uncertainties that are interpretable within the given field of science. Experimental data provide insight into the physical processes beyond what current physics models can provide, and data-driven UQ strengthens the predictive power and enables validated simulations. Embedding physical understanding of the system within the UQ analysis is crucial to meaningful interpretation of data in science-based applications. In this minisymposium, several applications of real world problems that necessitate UQ informed by both physical models and data will be presented.

10:30

Uncertainty Quantification Applied to Convolutional Neural Network Analyses

11:00

- CANCELED - Uncertainty Quantification is not Enough to Reach Confident Decisions

11:30

A Bayesian formulation for spatially varying multi-regularization image deblurring problems

12:00

- CANCELED - Quantifying uncertainty from multiple sources in calibrated models of sparse inertial confinement fusion experiments

10:30

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Robert Bassett | Naval Postgraduate School | United States

Mihai Anitescu | Argonne National Laboratory | United States

Noemi Petra | University of California, Merced | United States

MS801: Optimization and Estimation of Complex Systems under Uncertainty Part I of III: Estimation

Chair(s)
Thomas M. Surowiec (Philipps-Universität Marburg)

Drew P. Kouri (Sandia National Laboratories)

Drew P. Kouri (Sandia National Laboratories)

Room:
MW HS 1801

Topic:
Optimization and optimal control under uncertainty

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The overwhelming majority of modern applications in the natural sciences, engineering, and beyond require both statistical estimation to accurately quantify the behavior of unknown distributed parameters in complex systems as well as a means of making optimal decisions that are resilient to this uncertainty. In this minisymposium, we aim to connect researchers working in optimization of complex systems under uncertainty such as equilibrium problems, differential algebraic equations, and partial differential equations, with statisticians working in variational statistics, infinite-dimensional statistical estimation, and optimum experimental design.

11:00

Trend Filtering on Graphs for Exponential Families

11:30

Scalable Gaussian Process Analysis using Hierarchical Off-Diagonal Low Rank Linear Algebra

12:00

A-optimal design of large-scale Bayesian linear inverse problems under uncertainty

10:30

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Fred J. Hickernell | Illinois Institute of Technology | United States

Alexandra Gessner | University of Tuebingen | Germany

MS161: Probabilistic Numerical Methods for Cubature

Room:
MW HS 0350

Topic:
Probability Theory for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The central tenet of probabilistic numerics is that uncertainty due to discretisation can be formally modelled. For numerical cubature in the presence of a limited computational budget, it is natural to seek to exploit any contextual information that may be available on the integrand. Classical cubatures, such as spline-based or Gaussian cubatures, are able to exploit abstract mathematical information such as the number of continuous derivatives of the integrand. However, in situations where information of a more contextual and perhaps speculative nature is available to the analyst, the use of generic classical cubatures can be sub-optimal by failing to take this information into account. The language of probabilities provides one mechanism in which diverse contextual information about the integrand can be captured. Through the formalism of a stochastic process model, the analyst can encode both abstract mathematical information, such as the number of continuous derivatives of the integrand, and speciﬁc contextual information, such as the possibility of a local trend or a periodic component. This minisymposium focuses on the development of probabilistic methods for numerical cubature, showcasing approaches that are state-of-the-art in this nascent research field.

10:30

The Successes and Challenges of Automatic Bayesian Cubature

11:30

Integrals of linearly constrained Gaussians

10:30

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Kristin Kirchner | Seminar for Applied Mathematics, ETH Zurich | Switzerland

Matteo Croci | University of Oxford | United Kingdom

Laura Scarabosio | Technical University of Munich | Germany

David Bolin | King Abdullah University of Science and Technology (KAUST) | Saudi Arabia

MS501: Gaussian Random Fields in Forward and Inverse UQ: Analysis, Numerics and Data Assimilation (Part I of II)

Room:
MW HS 0250

Topic:
Statistical methods for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Gaussian Random Fields (GRFs) are ubiquitous models of random functions in computational UQ. Their efficient numerical treatment as input data for PDEs, for modeling in spatial statistics and as key building block within larger UQ simulation loops continues to receive attention in numerical analysis, spatial statistics, and scientific computing.

This mini-symposium will present contributions at the forefront of research, addressing among others the fast and compressive multilevel simulation of GRFs, the impact of formatted numerical linear algebra (H- and H2-Matrix formats, Quantized Tensor Trains) on GRF simulation and identification, efficient covariance estimation algorithms for GRFs, the interplay of massively parallel PDE solvers and GRFs, the multilevel Monte Carlo and Quasi-Monte Carlo integration for GRF PDE inputs, as well as statistical applications.

10:30

Fast simulation of non-stationary Gaussian random fields

11:00

MLQMC Methods for Elliptic PDEs Driven by White Noise

11:30

Boundary effects in PDE-based sampling of Gaussian Matérn random fields

12:00

- CANCELED - Spatial modeling of significant wave height using deformed SPDE models

10:30

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Valerio Lucarini | University of Reading and University of Hamburg | United Kingdom

Nils Berglund | Institut Denis Poisson (IDP), University of Orléans | France

Tobias Hurth | Université de Neuchâtel | Switzerland

Tony Lelievre | Ecole des Ponts ParisTech | France

MS181: Bifurcations and Uncertainty Quantification (Part I of II)

Chair(s)
Christian Kuehn (Technical University of Munich)

Grigorios A. Pavliotis (Imperial College London)

Grigorios A. Pavliotis (Imperial College London)

Room:
MW HS 1250

Topic:
Probability Theory for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Randomness in differential equations is a key topic to build and understand mathematical models arising in applications. Yet, most real-world problems are nonlinear and may exhibit complicated dynamics. A key concept to understand transitions in nonlinear systems are bifurcations. In this minisymposium we aim to bring together the two communities from uncertainty quantification and nonlinear dynamics more closely. The goal is to foster the interface and interaction between nonlinear systems theory and the role of randomness. In particular, we expect many important open questions to arise out of this exchange of ideas.

10:30

Global Stability Properties of the Climate: Melancholia States, Invariant Measures, and Phase Transitions

11:00

On Periodically Forced Stochastic Systems

11:30

Randomly Switched Vector Fields and Bifurcations

12:00

Computation of Sensitivities for the Invariant Measure of a Parameter Dependent Diffusion

10:30

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Clémentine Prieur | Grenoble Alpes University, LJK-lab, Inria research team AIRSEA | France

Sebastien Roux | INRA | France

Pierre Gremaud | NC State University, Department of Mathematics | United States

Maria Belen Heredia | Grenoble Alpes University, IRSTEA, LJK-lab | France

MS201: Sensitivity analysis for models with high-dimensional inputs and vectorial or functional outputs

Room:
MW 0608m

Topic:
UQ for complex systems

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Global sensitivity analysis (GSA) aims at quantifying how the uncertainty in the output quantity of interest (QoI) of mathematical models can be apportioned to uncertainties in the input model parameters. Specifically, variance-based GSA enables ranking the importance of model inputs by computing their relative contribution to the variance of the QoI, as quantified by Sobol’ indices. In the presence of dependences among input parameters, Sobol’ indices may be difficult to interpret and one may prefer to compute Shapley effects, which propose an equitable sharing of the model output variance, and which were originated as a solution concept in cooperative game theory. An alternative approach is derivative-based GSA, which allows an efficient screening for unimportant input parameters. In many applications, the models under study involve many input parameters, which are not necessarily independent, and produce several QoIs, which may be scalar, but also vectorial or even functional. The purpose of that MS is to present recent generalizations of both variance-based GSA and derivative-based GSA in that framework. These recent results are validated on toy models but also applied to large-scale applications, such as high dimensional neuroscience models or avalanche models.

10:30

Gradient-based dimension reduction of multivariate vector-valued functions

11:00

Using fuzzy clustering for improving the interpretability of multivariate sensitivity analysis

11:30

Global sensitivity analysis of high dimensional neuroscience models

12:00

Aggregated Shapley effects from an acceptance-rejection sample: application to an avalanche model

10:30

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Matthew Plumlee | Northwestern University | United States

Faezeh Yazdi | Department of Statistics and Actuarial Science, Simon Fraser University | Canada

Earl Lawrence | Statistical Sciences, Los Alamos National Laboratory | United States

Jarad Niemi | Department of Statistics, Iowa State University | United States

MS241: Computer model calibration for large data sets, with applications

Room:
MW ZS 1050

Topic:
Model error, discrepancy and calibration

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Emulation and calibration has proven to be a fruitful way to get the most from simulators. The usual approach for combining simulators with field data has 3 main goals; (i) building a predictive model; (ii) estimating calibration parameters that govern the system; and (iii) estimating the discrepancy between the response surface mean of the system and the computer model.

Increasingly, there are cases where the model is fast but the code is not readily available, but a large suite of model evaluations is available. In other cases, the code is fast, but the object being simulated is so large that it cannot be saved. That is, one cannot save what was computed (a more common problem as exa-scale computers come online). In either case, experimenters are faced with having to construct an emulator of the computer model to stand in for the simulator.

The common approach for emulation uses a Gaussian process (GP). Unfortunately, it is computationally intractable when the number of evaluations is large and a poor choice for non-smooth functions.

This mini-symposium presents new approaches for emulation and calibration for large computer experiments. There are 4 talks: (i) an overview of the issues and new large sample approximations for fast calibration; (ii) non-stationary deep GPs for calibration of a model of binary black hole mergers, with variational inference; (iii) calibration in exa-scale computing with new in-situ analyses; and (iv) knot-based methods for fast GPs.

10:30

Fast parameter calibration and prediction with large computer experiments

11:00

Deep Gaussian process calibration for binary black hole mergers

11:30

In Situ inference for exascale uncertainty quantification

12:00

One-at-a-time knot selection for approximate Gaussian processes

10:30

iCal
Joost Opschoor | ETH Zürich | Switzerland

Reinhold Schneider | TU Berlin | Germany

Teo Deveney | University of Bath | United Kingdom

MS191: Deep Learning Algorithms in Computational UQ (Part I of II)

Room:
MW ZS 2050

Topic:
Physics-informed and data-driven UQ methods

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

This MS will explore recent advances and ongoing foundational (mathematical and statistical) work alongside with recent algorithmic advances in the use of Deep Learning (DL) algorithms in computational UQ.

Contributions address in particular:

* approximation rate estimates for DL algorithms applied to solution manifolds of high-dimensional, parametric PDEs,

* DL accelerated sampling algorithms for UQ in inverse problems,

* Invertible DL surrogates of high-dimensional probability densities,

* DL architectures suitable in UQ,

* physics-informed DL algorithms for learning UQ in parametric PDE models,

* expressivity of DL algorithms for PDE constrained optimization and Bayesian inversion.

11:00

Deep neural network expression rate analysis for forward and Bayesian inverse PDE UQ

11:30

A theoretical analysis of deep neural networks and parametric PDEs

12:00

A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models

10:30

iCal
Andrew Roberts | Los Alamos National Laboratory | United States

Brendan A. West | Cold Regions Research and Engineering Laboratory | United States

Nora Loose | Oden Institute for Computational Engineering and Sciences & Jackson School of Geosciences, The University of Texas at Austin | United States

Ellen Buckley | University of Maryland, Department of Atmospheric and Oceanic Science | United States

MS611: Uncertainty estimates of the cryosphere and its forcings (Part I of III)

Chair(s)
Andrew Davis (Cold Regions Research and Engineering Laboratory)

Nicole-Jeanne Schlegel (Jet Propulsion Laboratory, California Institute of Technology)

Nicole-Jeanne Schlegel (Jet Propulsion Laboratory, California Institute of Technology)

Room:
MW ZS 1450

Topic:
UQ for complex systems

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The cryosphere and the processes that force its evolution have profound and permanent effects on the global climate. In particular, Arctic amplification leads to extreme mid-latitude weather and glacier and ice sheet retreat is increasing global mean sea level causing the ocean to encroach onto coastal communities. Despite potentially devastating impacts, accurate predictions of future dynamics and rigorous characterizations of the associated uncertainty remain elusive. Misunderstood physics and computational limitations require complex physical processes to be parameterized and calibrated using noisy data that is sparse in both space and time. However, collecting data in remote polar regions is difficult, dangerous, and expensive. Therefore, we must leverage remote sensing techniques and wisely allocate limited resources. Finally, predictive uncertainties must be quantified to give meaningful error bounds on quantities of interest, such as future mean sea level. This session discusses recent advancements trying to understand the dynamic processes governing the cryosphere given observations and/or models as well as techniques to obtain and analyze data.

10:30

Quantifying the Skill and Bias of Arctic Sea Ice Simulations

11:00

Forcing meshfree simulations of sea ice in the Nares Strait using uncertain wind current data

11:30

Observe Greenland remotely from the subpolar North Atlantic? Uncertainty quantified in a large-scale oceanographic inverse problem

12:00

High-resolution Remote Sensing Sea Ice Observations for Improved Prediction

10:30

iCal
Tiernan Casey | Sandia National Laboratories | United States

Zhen Chen | Ohio State University | United States

Krishna Garikipati | University of Michigan | United States

Houman Owhadi | California Institute of Technology | United States

MS261: Machine Learning for Systems with Uncertainty and Noise (Part I of II)

Room:
MW ZS 1550

Topic:
Physics-informed and data-driven UQ methods

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The fast development of machine learning (ML) has resulted in explosive advancement in many aspects of science and engineering. In particular, deep learning methods allow one to understand highly complex systems that are almost impossible to study by the traditional methods. Inevitably, uncertainty and noise are ubiquitous in such complex systems. And the design and application of ML methods need to take into account of these uncertainties. On the other hand, modern ML methods also provide new perspectives and algorithm design possibilities for uncertainty quantification. It is the interface of ML and UQ that this mini-symposium focuses on. Our aim is to bring together a group of experts in ML and UQ and to foster knowledge exchange. Our primary focus is on two front: (1) how uncertainty can be quantified in modern ML methods; and (2) how modern ML methodology can help UQ to further our understanding and advance algorithm design.

10:30

Uncertainty quantification in chaotic systems

11:00

Governing Equations Recovery for Systems with Uncertainties

11:30

Variational system identification of partial differential equations governing pattern formation: Inference with sparse, noisy and incomplete data

12:00

Kernel mode decomposition and regression networks

10:30

iCal
Omar Ghattas | The University of Texas at Austin | United States

Jayanth Jagalur-Mohan | Massachusetts Institute of Technology | United States

Yiolanda Englezou | University of Cyprus | Cyprus

Lior Horesh | IBM and Columbia University | United States

MS701: Advances in Bayesian optimal experimental design (Part I of II)

Chair(s)
Peng Chen (The University of Texas at Austin)

Omar Ghattas (The University of Texas at Austin)

Omar Ghattas (The University of Texas at Austin)

Room:
MW 2250

Topic:
Design of experiments

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The goal of optimal experimental design (OED) is to find the optimal design of a data acquisition system (e.g., location of sensors, what quantities are measured and how often, what sources are used in each experiment), so that the uncertainty in the inferred parameters—or some predicted quantity derived from them—is minimized with respect to a statistical criterion. OED for Bayesian inverse problems governed by partial differential equations (PDEs) is an extremely challenging problem. First, the parameter to be inferred is often a spatially correlated field, leading to a high dimensional parameter space upon discretization. Second, the forward PDE model is often complex and computationally expensive to solve. Third, the design space for the data acquisition system may be high dimensional and constrained. And fourth, the Bayesian inverse problem—a difficult problem in itself—is a part of the OED formulation and needs to be repeated many times. This minisymposium brings together leading experts to present recent advances in numerical methods for Bayesian OED that address these difficulties.

10:30

Scalable structure-exploiting approaches to optimal experimental design

11:00

Bayesian experimental design in high-dimensional settings: A point process approach

11:30

Bayesian design of experiments for the calibration of computational models

12:00

Optimal experimental design for symbolic regression

10:30

iCal
Jeremy Oakley | University of Sheffield | United Kingdom

Emanuele Borgonovo | Bocconi University | Italy

Mauricio Monsalve | Research Center for Integrated Disaster Risk Management (CIGIDEN) | Chile

Thais Fonseca | University of Warwick | United Kingdom

MS221: Decision making under uncertainty (Part I of II)

Chair(s)
Daniel Straub (Technical University of Munich)

Jeremy Oakley (University of Sheffield)

Iason Papaioannou (Technical University of Munich)

Jeremy Oakley (University of Sheffield)

Iason Papaioannou (Technical University of Munich)

Room:
MW 1701

Topic:
Other

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Results from UQ ultimately serve as decision support. Hence it is relevant to set the UQ analysis in the context of a formal decision analysis, to ensure the optimal choice of UQ methods and interpretation of results. This minisymposium focuses on such a combination of UQ with formal decision analysis methods. On the one hand, this includes the selection of metrics for UQ analysis based on decision-theoretic considerations. Examples include the choice of appropriate objective functions and decision-theoretic sensitivity measures. On the other hand, the minisymposium considers the integration of UQ in artificial intelligence applications, and more specifically sequential decision making algorithms, which are of increasing relevance in many fields of application.

10:30

- CANCELED - Global sensitivity analysis and reducing input uncertainty

11:00

Information Density in the Global Sensitivity Analysis of Computer Experiments

11:30

Mitigating epistemic bias in Bayesian statistical model combination, with examples from natural disaster research

12:00

Communicating uncertainty for decision support

10:30

iCal
Damiano Lombardi | Inria Paris and Sorbonne Université / LJLL | France

Marie Billaud Friess | Centrale Nantes / LMJL | France

Donsub Rim | NYU / Courant Institute of Mathematical Sciences | United States

Stephan Rave | University of Münster | Germany

MS311: Dynamical low rank and reduced basis methods for random or parametric time dependent problems (Part I of II)

Chair(s)
Fabio Nobile (EPFL)

Marie Billaud Friess (Centrale Nantes / LMJL)

Anthony Nouy (Centrale Nantes / LMJL)

Marie Billaud Friess (Centrale Nantes / LMJL)

Anthony Nouy (Centrale Nantes / LMJL)

Room:
IAS 0.001

Topic:
Reduced order models

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Model order reduction is an effective strategy to address problems with (possibly random) parameters. The idea is to project the underlying equations onto a small finite-dimensional subspace spanned by few cleverly constructed deterministic modes thus leading to a reduced size problem on which UQ or parametric analysis can be cheaply performed by e.g. sampling or quadrature techniques.

However, for time-dependent problems with complex dynamics, the optimal subspace on which to approximate the solution at each time instant can considerably change over time. We address in this minisymposium recent dynamical techniques to construct time-varying reduced subspaces. These include, for instance, local-adaptive-transformed reduced basis methods as well as dynamical low-rank tensor approximations.

10:30

Hierarchical Partitioning Format for adaptive dynamical low rank approximations

11:00

Dynamical low-rank approximation method for linear conservation laws

11:30

Manifold Approximation via Transported Spaces (MATS)

12:00

Reduced Basis Approximation of Problems with Moving Discontinuities via Nonlinear State Space Transformation

10:30

iCal
Kathryn Maupin | Sandia National Laboratories | United States

Khachik Sargsyan | Sandia National Laboratories | United States

Teresa Portone | The University of Texas at Austin | United States

Rebecca Morrison | University of Colorado, Boulder | United States

MS491: Approaches to quantifying model-form uncertainty (Part I of II)

Chair(s)
Kathryn Maupin (Sandia National Laboratories)

Teresa Portone (The University of Texas at Austin)

Teresa Portone (The University of Texas at Austin)

Room:
IAS 4.001

Topic:
Model error, discrepancy and calibration

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Model-form uncertainty remains a concern in all areas of mathematical modeling. Computational models are increasingly used to make predictions affecting high-consequence engineering design and policy decisions. Incomplete information about the phenomenon being represented and limitations in computational resources require approximations and simplifications that can lead to uncertainties in the model’s form and errors in predicted quantities of interest. Techniques to address these uncertainties are essential for understanding the reliability of such predictions. Furthermore, they have the potential to increase the range of applicability and enhance the predictive power of uncertain models. Development of these approaches is an active area of research and is often necessarily application-specific. This minisymposium brings together researchers from a variety of disciplines to discuss different methods of addressing model-form uncertainty, including Bayesian and non-Bayesian approaches.

*Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

10:30

- CANCELED - Calibration, Propagation, and Validation of Model Discrepancy Across Experimental Settings

11:00

Embedded model error quantification and propagation in physical models

11:30

- CANCELED - Characterizing model-form uncertainty in an inadequate model of anomalous transport

12:00

Representing model error in reduced models of interacting systems

10:30

iCal
Marko Laine | Finnish Meteorological Institute | Finland

Jessica Matthews | North Carolina State University | United States

Zhengyuan Zhu | Iowa State University | United States

Johanna Tamminen | Finnish Meteorological Institute | Finland

MS421: Data Fusion in Remote Sensing

Room:
Interims Lecture Hall 101

Topic:
Statistical methods for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Global satellite observations have become a crucial data source for environmental and climate research, services and applications. While important findings can be done by analysing single data sets or singe overpass separately, enormous possibilities for research breakthroughs are expected by combining various data sets together. The uncertainty quantification of data fusion needs to take into account several data sources with various underlying assumptions and typically different resolutions, scales and temporal coverage.

This mini-session focuses on recent achievements in the spatio-temporal data fusion methodologies and applications in satellite remote sensing.

10:30

Large scale data fusion system of remote sensing and in situ observations by dimension reduction

11:00

Land surface albedo observations from a constellation of geostationary satellites

11:30

- CANCELED - Model based pixel-level image fusion for remote sensing

12:00

Anomalies as a tool in detecting hidden features in satellite data sets

10:30

iCal
Franca Hoffmann | California Institute of Technology | United States

Neil Chada | NUS | Singapore

Ricardo Baptista | Massachusetss Insitute of Technology | United States

Daniel Sanz-Alonso | University of Chicago | United States

MS381: Bridges between Data assimilation and Machine Learning (Part I of II)

Room:
Interims Lecture Hall 102

Topic:
Data assimilation

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

The task of processing large amounts of data in order to model complex associated dynamical systems is an important challenge of the 21st century. The need for novel mathematical concepts and advanced computational techniques in this context has accelerated research in the associated fields of Data Assimilations and Machine Learning. In recent years the two research communities have been growing closer resulting in advanced numerical methods that combine the strength of both worlds and the development of theoretical underpinning of existing and new techniques. The aim of this MS is to foster these emerging bridges, to detect limitations and possible future alleys by bringing together people from both communities and creating a room for scientific exchange.

10:30

Kalman-Wasserstein Gradient Flows

11:00

Quantifying ensemble Kalman inversion as a derivative-free optimizer

11:30

Nonlinear ensemble filtering and smoothing via couplings

12:00

Graph methods for semi-supervised learning and Bayesian inverse problems

10:30

iCal
Andrés Felipe López-Lopera | Ecole Nationale Supérieure des Mines de Saint-Etienne | France

Simon Mak | Duke University | United States

Cédric Travelletti | Idiap Research Institute and University of Bern | Switzerland

MS231: Incorporating structural information in kernel methods for prediction and design space exploration (Part I of II)

Room:
Exzellenzzentrum 0003

Topic:
Statistical methods for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Probabilistic and deterministic kernel methods have proven very useful and versatile for a number of classification,

density estimation, and prediction problems arising in science and society. Yet, these methods are often considered as black boxes, and the fantastic expressiveness allowed by the choice of the underlying positive definite kernel is classically underestimated. This double minisymposium gathers researchers from various horizons who have been investigating the incorporation of physical and other structural information in kernel methods in contexts such as Gaussian Process (GP) modelling, adaptive Bayesian integration, space-filling design with minimum energy measures versus maximum mean discrepancy, and probabilistic prediction of probability density fields. In Part I, the emphasis will be put on the incorporation of physical laws and boundary information in GP-related models, with applications in a number of fields encompassing in particular electromagnetism, mechanics, geophysics and biology. In Part II, the focus will be put more specifically on kernels and distances for space-filling design, image-valued GP modelling, high-dimensional integration, and assessing predictions of probability density fields by spatial logistic Gaussian and related models.

11:00

Physically-inspired Gaussian processes with application to biology

11:30

BdryGP: a new Gaussian process model for incorporating boundary information

12:00

End to end GP-based inversion of a mass density field from gravimetric measurements

14:00

iCal
Max Morris | Iowa State University | United States

MT02: Max Morris: Introduction to Experimental Design: Field Trials to Finite Elements

Room:
MW HS 2001

Topic:
Design of experiments

Form of presentation:
Mini-tutorial

Duration:
120 Minutes

14:00

Introduction to Experimental Design: Field Trials to Finite Elements

14:00

iCal
Oana Marin | Argonne National Laboratory | United States

Nikolaus Adams | Technical University of Munich | Germany

Luca Magri | University of Cambridge | United Kingdom

Marieme Ngom | Argonne National Laboratory | United States

MS631: Uncertainties in multiphase flow models

Room:
MW HS 0001

Topic:
UQ for complex systems

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Multiphase flows are subject to various modeling and numerical approximations which induce uncertainty in the reliability of overall dynamics. A notorious difficulty is the modeling of interfaces between two different fluids with different material properties, or reactive shock-bubble interactions. Accounting also for the dependency on experimentally determined parameters, multiphase flow models involve a wealth of uncertainties from algorithmic, epistemic to structural uncertainty. Fluid-particle interactions, on the other hand, may alter the very essence of the flow equations, inducing chaos in both experiments and simulations, even for linear reversible flows. The onset of chaos poses the question of state uncertainty, which is representative also for nonlinear flows. The complexity of multi-phase models is exhibited also at the computational level, rendering the study of uncertainty cumbersome also from a numerical standpoint, which subsequently demands either efficient algebraic treatments or the development of reduced-order models. We seek to present various approaches to these issues, on both academic examples and at-scale engineering problems, evaluating traditional uncertainty quantification methods as well as incorporating novel strategies such as probabilistic programming and hierarchical reinforcement learning.

14:00

Uncertainty quantification of the loss of reversibility in Stokesian dynamics

14:30

Intelligent Inference for Multiphase Flows

15:00

Data assimilation in multi-physical flows for the design of aeronautical propulsion systems

15:30

Impact of surface tension uncertainty on an immiscible rising bubble

14:00

iCal
Drew P. Kouri | Sandia National Laboratories | United States

Kerem Ugurlu | Nazarbayev University | Kazakhstan

Johannes Milz | Technical University of Munich | Germany

MS802: Optimization and Estimation of Complex Systems under Uncertainty Part II of III: Optimization

Chair(s)
Thomas M. Surowiec (Philipps-Universität Marburg)

Drew P. Kouri (Sandia National Laboratories)

Drew P. Kouri (Sandia National Laboratories)

Room:
MW HS 1801

Topic:
Optimization and optimal control under uncertainty

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The overwhelming majority of modern applications in the natural sciences, engineering, and beyond require both statistical estimation to accurately quantify the behavior of unknown distributed parameters in complex systems as well as a means of making optimal decisions that are resilient to this uncertainty. In this minisymposium, we aim to connect researchers working in optimization of complex systems under uncertainty such as equilibrium problems, differential algebraic equations, and partial differential equations, with statisticians working in variational statistics, infinite-dimensional statistical estimation, and optimum experimental design.

14:00

Risk-Adapted Optimal Experimental Design

15:00

Robust Utility Maximization with Drift and Volatility Uncertainty

15:30

An Approximation Scheme For Distributionally Robust PDE-Constrained Optimization

14:00

iCal
Tim Sullivan | Free University of Berlin | Germany

Philipp Hennig | University of Tübingen & Max Planck Institute for Intelligent Systems | Germany

Chris Oates | Newcastle University | United Kingdom

Maren Mahsereci | Amazon | United Kingdom

MS051: Probabilistic Numerical Methods: Opportunities and Challenges

Room:
MW HS 0350

Topic:
Probability Theory for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Probabilistic numerical methods (PNM) explicitly model uncertainty arising in numerical computations. Early foundations to the field reach back more than a century. However, only in the past few decades, with ever-increasing computing resources and data volume, have the needs and tools for uncertainty-aware computation arisen. Uncertainty quantification for numerical methods is promising if not essential in various modern scientific computing tasks. For example, large physically relevant PDE systems cannot be affordably solved to arbitrary accuracy, and contemporary Big Data applications give rise to stochastic problems that classical methods fail to solve, calling for new algorithms. A probabilistic treatment of numerical methods can further allow for adaptive methods and problem-specific decision-making in expensive models that do not allow for many realizations. A yet understudied challenge to be addressed in future PNM is the treatment of numerical uncertainty in pipelines of computations. These directions require a thorough theoretical understanding as well as easy-to-apply black-box algorithms for the practitioner. This minisymposium takes a holistic approach to PNM and serves to survey recent advances and establish future research directions.

14:00

Probabilistic numerics: History and recent trends

14:30

It is time to take Uncertainty Seriously

15:00

Fast Bayesian Inference for Differential Equations Using Probabilistic Numerical Methods

15:30

Active Multi-Source Bayesian Quadrature for Expensive Simulations

14:00

iCal
Markus Bachmayr | Johannes Gutenberg-Universität Mainz | Germany

Olga Moreva | Daimler AG, Werk Sindelfingen | Germany

Jonas Latz | University of Cambridge | United Kingdom

MS502: Gaussian Random Fields in Forward and Inverse UQ: Analysis, Numerics and Data Assimilation (Part II of II)

Room:
MW HS 0250

Topic:
Statistical methods for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Gaussian Random Fields (GRFs) are ubiquitous models of random functions in computational UQ. Their efficient numerical treatment as input data for PDEs, for modeling in spatial statistics and as key building block within larger UQ simulation loops continues to receive attention in numerical analysis, spatial statistics, and scientific computing.

This mini-symposium will present contributions at the forefront of research, addressing among others the fast and compressive multilevel simulation of GRFs, the impact of formatted numerical linear algebra (H- and H2-Matrix formats, Quantized Tensor Trains) on GRF simulation and identification, efficient covariance estimation algorithms for GRFs, the interplay of massively parallel PDE solvers and GRFs, the multilevel Monte Carlo and Quasi-Monte Carlo integration for GRF PDE inputs, as well as statistical applications.

14:00

Multilevel representations of stationary Gaussian random fields and efficient sampling methods

14:30

Fast and exact simulation of univariate and bivariate Gaussian random fields

15:30

Fast sampling of parameterised Gaussian random fields

14:00

iCal
Maxime Breden | École Polytechnique | France

Shi Jin | Shanghai Jiao Tong University | China

Maximilian Engel | Technical University of Munich | Germany

Xiaozhu Zhang | Technical University Dresden | Germany

MS182: Bifurcations and Uncertainty Quantification (Part II of II)

Chair(s)
Christian Kuehn (Technical University of Munich)

Grigorios A. Pavliotis (Imperial College London)

Grigorios A. Pavliotis (Imperial College London)

Room:
MW HS 1250

Topic:
Probability Theory for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Randomness in differential equations is a key topic to build and understand mathematical models arising in applications. Yet, most real-world problems are nonlinear and may exhibit complicated dynamics. A key concept to understand transitions in nonlinear systems are bifurcations. In this minisymposium we aim to bring together the two communities from uncertainty quantification and nonlinear dynamics more closely. The goal is to foster the interface and interaction between nonlinear systems theory and the role of randomness. In particular, we expect many important open questions to arise out of this exchange of ideas.

14:00

Computing Invariant Sets of Random Differential Equations using Polynomial Chaos

14:30

- CANCELED - Random Batch methods for interacting particle systems and for high dimensional global minimization problems in machine learning

15:00

A Random Dynamical Systems Perspective on Isochronicity for Stochastic Oscillators

15:30

- CANCELED - Dynamic Vulnerability in Oscillatory Networks and Power Grids

14:00

iCal
Elmar Plischke | Institute of Disposal Reasearch, Clausthal University of Technology | Germany

Kévin Elie-Dit-Cosaque | Université Lyon 1, SCOR SE | France

Baptiste Broto | CEA, LIST, Université Paris-Saclay | France

Xuefei Lu | Department of Energy, Politecnico di Milano and Department of Decision Sciences, Bocconi University, Milan | Italy

MS211: Green Sensitivity: measures from available input/output data

Room:
MW 0608m

Topic:
UQ for complex systems

Form of presentation:
Mini-symposium

Duration:
120 Minutes

In most application fields, analysts support their investigations through the use of quantitative models. Over the years, the computation of sensitivity (or importance) measures has become an integrating part of the analysis, allowing analysts to obtain insights on key drivers of model behavior either on a local or on a global scale. However, the estimation of global sensitivity measures may become a computationally challenging task, especially when the number of model inputs is large and the model output is sparse. Moreover, the estimation is considered even more challenging when the sensitivity measures require to look at the entire model output distribution. Most of the algorithms to compute sensitivity measures require special sampling schemes or additional model evaluations so that available data from previous model runs (e.g., from an uncertainty analysis based on Latin Hypercube Sampling) cannot be reused. In this MS, we are interested in the challenging task of estimating global sensitivity measures by recycling an available finite set of input/output data. Green sensitivity, by recycling, avoids wasting. Different global sensitivity measures are proposed by the authors, depending on the application field and the associated constraints: moment-independent sensitivity measures, quantile-oriented sensitivity measures, Shapley effects. Various statistical estimation procedures are studied, such as, e.g., nearest-neighbour techniques, random forest or bayesian statistics.

14:00

Integrated Distribution Functions (with Friends and Relatives) for Sensitivity Analysis

14:30

Random forest-based estimation of Quantile Oriented Sensitivity Analysis

15:00

Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution

15:30

- CANCELED - Bayesian Estimation of Probabilistic Sensitivity Measures for Computer Experiments

14:00

iCal
Matthias Katzfuss | Texas A&M University | United States

Andrew Zammit Mangion | University of Wollongong | Australia

William Kleiber | University of Colorado | United States

Finn Lindgren | University of Edinburgh | United Kingdom

MS541: Scalable uncertainty quantification in spatial statistics

Room:
MW ZS 1050

Topic:
Statistical methods for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Uncertainty quantification is of paramount importance in many geospatial applications. For example, data from remote-sensing platforms are often incomplete, noisy, and indirect. Spatial statistical methods, often based on Gaussian-process models, can address many of these challenges, but a major challenge is the computational infeasibility for large datasets. This session will discuss recent developments in spatial statistics for scalable uncertainty quantification.

14:00

Gaussian-process approximations for big data

14:30

Uncertainty quantification of environmental processes from large spatial data

15:00

Mixed graphical-basis models for large nonstationary and multivariate spatial data problems

15:30

Scalable latent spatio-temporal non-Gaussian process models via Gaussian stochastic PDEs

14:00

iCal
Lars Grasedyck | RWTH Aachen | Germany

Jakob Zech | Massachusetts Institute of Technology | United States

Roberto Molinaro | ETH Zürich | Switzerland

MS192: Deep Learning Algorithms in Computational UQ (Part II of II)

Room:
MW ZS 2050

Topic:
Physics-informed and data-driven UQ methods

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

This MS will explore recent advances and ongoing foundational (mathematical and statistical) work alongside with recent algorithmic advances in the use of Deep Learning (DL) algorithms in computational UQ.

Contributions address in particular:

* approximation rate estimates for DL algorithms applied to solution manifolds of high-dimensional, parametric PDEs,

* DL accelerated sampling algorithms for UQ in inverse problems,

* Invertible DL surrogates of high-dimensional probability densities,

* DL architectures suitable in UQ,

* physics-informed DL algorithms for learning UQ in parametric PDE models,

* expressivity of DL algorithms for PDE constrained optimization and Bayesian inversion.

14:30

Learning Deep Neural Networks via Hierarchical Low Rank Tensors

15:00

Posterior density approximation for Gaussian priors

15:30

A multi-level procedure for enhancing accuracy of machine learning algorithms

14:00

iCal
Xavier Fettweis | University of Liège | Belgium

Torsten Albrecht | Potsdam Institute for Climate Impact Research | Germany

Lambert Caron | Jet Propulsion Laboratory | United States

Nicole-Jeanne Schlegel | Jet Propulsion Laboratory | United States

MS612: Uncertainty estimates of the cryosphere and its forcings (Part II of III)

Chair(s)
Nicole-Jeanne Schlegel (Jet Propulsion Laboratory, California Institute of Technology)

Andrew Davis (Cold Regions Research and Engineering Laboratory)

Andrew Davis (Cold Regions Research and Engineering Laboratory)

Room:
MW ZS 1450

Topic:
UQ for complex systems

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The cryosphere and the processes that force its evolution have profound and permanent effects on the global climate. In particular, Arctic amplification leads to extreme mid-latitude weather and glacier and ice sheet retreat is increasing global mean sea level causing the ocean to encroach onto coastal communities. Despite potentially devastating impacts, accurate predictions of future dynamics and rigorous characterizations of the associated uncertainty remain elusive. Misunderstood physics and computational limitations require complex physical processes to be parameterized and calibrated using noisy data that is sparse in both space and time. However, collecting data in remote polar regions is difficult, dangerous, and expensive. Therefore, we must leverage remote sensing techniques and wisely allocate limited resources. Finally, predictive uncertainties must be quantified to give meaningful error bounds on quantities of interest, such as future mean sea level. This session discusses recent advancements trying to understand the dynamic processes governing the cryosphere given observations and/or models as well as techniques to obtain and analyze data.

14:00

SMBMIP: Intercomparison of modelled 1980-2012 surface mass balance over the Greenland Ice sheet

14:30

Projecting Antarctica’s contribution to future sea level rise from basal ice-shelf melt using linear response functions of 16 ice sheet models (LARMIP-2)

15:00

GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science

15:30

Quantification of Surface forcing Requirements for a Greenland Ice Sheet Model using Uncertainty Analyses

14:00

iCal
Henning Lange | University of Washington | United States

Nicholas Geneva | University of Notre Dame | United States

Assad Oberai | University of Southern California | United States

Weize Mao | Ohio State University | United States

MS262: Machine Learning for Systems with Uncertainty and Noise (Part II of II)

Room:
MW ZS 1550

Topic:
Physics-informed and data-driven UQ methods

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The fast development of machine learning (ML) has resulted in explosive advancement in many aspects of science and engineering. In particular, deep learning methods allow one to understand highly complex systems that are almost impossible to study by the traditional methods. Inevitably, uncertainty and noise are ubiquitous in such complex systems. And the design and application of ML methods need to take into account of these uncertainties. On the other hand, modern ML methods also provide new perspectives and algorithm design possibilities for uncertainty quantification. It is the interface of ML and UQ that this mini-symposium focuses on. Our aim is to bring together a group of experts in ML and UQ and to foster knowledge exchange. Our primary focus is on two front: (1) how uncertainty can be quantified in modern ML methods; and (2) how modern ML methodology can help UQ to further our understanding and advance algorithm design.

14:00

Long-Time Forecasting Methods Leveraging Machine Learning

14:30

Surrogate Modeling and Uncertainty Quantification of Dynamical PDE Systems with Bayesian Physics-Constrained Deep Neural Networks

15:00

Deep generative priors for quantifying uncertainty

15:30

Learning Moment Equations using Measurement Data

14:00

iCal
Fengyi Li | Massachusetts Institute of Technology | United States

Luis Espath | RWTH Aachen University | Germany

Dia Ben Mansour | King Fahd University of Petroleum and Minerals | Saudi Arabia

Ekaterina Kostina | Heidelberg University | Germany

MS702: Advances in Bayesian optimal experimental design (Part II of II)

Chair(s)
Omar Ghattas (The University of Texas at Austin)

Peng Chen (The University of Texas at Austin)

Peng Chen (The University of Texas at Austin)

Room:
MW 2250

Topic:
Design of experiments

Form of presentation:
Mini-symposium

Duration:
120 Minutes

The goal of optimal experimental design (OED) is to find the optimal design of a data acquisition system (e.g., location of sensors, what quantities are measured and how often, what sources are used in each experiment), so that the uncertainty in the inferred parameters—or some predicted quantity derived from them—is minimized with respect to a statistical criterion. OED for Bayesian inverse problems governed by partial differential equations (PDEs) is an extremely challenging problem. First, the parameter to be inferred is often a spatially correlated field, leading to a high dimensional parameter space upon discretization. Second, the forward PDE model is often complex and computationally expensive to solve. Third, the design space for the data acquisition system may be high dimensional and constrained. And fourth, the Bayesian inverse problem—a difficult problem in itself—is a part of the OED formulation and needs to be repeated many times. This minisymposium brings together leading experts to present recent advances in numerical methods for Bayesian OED that address these difficulties.

14:00

A sample-driven transport approach to Bayesian optimal experimental design

14:30

Multilevel Double-loop Monte Carlo and its counterpart in stochastic collocation

15:00

Power posterior implicit sampling for the expected information gain in Bayesian experimental design

15:30

Optimal Experimental Design Problem as Mixed-integer Optimal Control Problem

14:00

iCal
Daniel Straub | Technical University of Munich | Germany

Matteo Pozzi | Carnegie Mellon University | United States

Hailiang Du | Durham University | United Kingdom

Costas Papadimitriou | University of Thessaly | Greece

MS222: Decision making under uncertainty (Part II of II)

Chair(s)
Daniel Straub (Technical University of Munich)

Jeremy Oakley (University of Sheffield)

Iason Papaioannou (Technical University of Munich)

Jeremy Oakley (University of Sheffield)

Iason Papaioannou (Technical University of Munich)

Room:
MW 1701

Topic:
Other

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Results from UQ ultimately serve as decision support. Hence it is relevant to set the UQ analysis in the context of a formal decision analysis, to ensure the optimal choice of UQ methods and interpretation of results. This minisymposium focuses on such a combination of UQ with formal decision analysis methods. On the one hand, this includes the selection of metrics for UQ analysis based on decision-theoretic considerations. Examples include the choice of appropriate objective functions and decision-theoretic sensitivity measures. On the other hand, the minisymposium considers the integration of UQ in artificial intelligence applications, and more specifically sequential decision making algorithms, which are of increasing relevance in many fields of application.

14:00

Quantifying and communicating uncertainty in probabilistic predictions for effective decision support

14:30

Sequential decision making under epistemic constraints: how to evaluate long-term policies and assess the value of information

15:00

Optimization-based Decision Support via Uncertainty Quantification

15:30

Methodology for Robust Bayesian Optimal Experimental Design Decisions

14:00

iCal
Antonio Falcó Montesinos | Universidad CEU Cardenal Herrera | Spain

Eva Vidlicková | Ecole Polytechnique Fédérale de Lausanne / CSQI-MATH | Switzerland

Gerrit Welper | University of Central Florida | United States

Philipp Schulze | Technische Universität Berlin | Germany

MS312: Dynamical low rank and reduced basis methods for random or parametric time dependent problems (Part II of II)

Chair(s)
Fabio Nobile (EPFL)

Marie Billaud Friess (Centrale Nantes / LMJL)

Anthony Nouy (Centrale Nantes / LMJL)

Marie Billaud Friess (Centrale Nantes / LMJL)

Anthony Nouy (Centrale Nantes / LMJL)

Room:
IAS 0.001

Topic:
Reduced order models

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

Model order reduction is an effective strategy to address problems with (possibly random) parameters. The idea is to project the underlying equations onto a small finite-dimensional subspace spanned by few cleverly constructed deterministic modes thus leading to a reduced size problem on which UQ or parametric analysis can be cheaply performed by e.g. sampling or quadrature techniques. However, for time-dependent problems with complex dynamics, the optimal subspace on which to approximate the solution at each time instant can considerably change over time. We address in this minisymposium recent dynamical techniques to construct time-varying reduced subspaces. These include, for instance, local-adaptive-transformed reduced basis methods as well as dynamical low-rank tensor approximations.

14:00

Tensor Methods for Model Reduction of Dynamical Systems

14:30

Time discretization and stability estimates for dynamical low rank approximations of random parabolic equations

15:00

Reduced Order Modeling for Hyperbolic PDEs with Shock Collisions

15:30

Model Reduction for Nonlinear Parameterized Transport Equations

14:00

iCal
Heng Xiao | Virginia Tech | United States

Vishal Srivastava | University of Michigan | United States

Yan Wang | Georgia Institute of Technology | United States

Chi Feng | Massachusetts Institute of Technology | United States

MS492: Approaches to quantifying model-form uncertainty (Part II of II)

Chair(s)
Kathryn Maupin (Sandia National Laboratories)

Teresa Portone (The University of Texas at Austin)

Teresa Portone (The University of Texas at Austin)

Room:
IAS 4.001

Topic:
Model error, discrepancy and calibration

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Model-form uncertainty remains a concern in all areas of mathematical modeling. Computational models are increasingly used to make predictions affecting high-consequence engineering design and policy decisions. Incomplete information about the phenomenon being represented and limitations in computational resources require approximations and simplifications that can lead to uncertainties in the model’s form and errors in predicted quantities of interest. Techniques to address these uncertainties are essential for understanding the reliability of such predictions. Furthermore, they have the potential to increase the range of applicability and enhance the predictive power of uncertain models. Development of these approaches is an active area of research and is often necessarily application-specific. This minisymposium brings together researchers from a variety of disciplines to discuss different methods of addressing model-form uncertainty, including Bayesian and non-Bayesian approaches.

*Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

14:00

A random matrix approach for quantifying model-form uncertainties in turbulence modeling

14:30

Improving generalization capabilities of physics-constrained data-augmented models

15:00

- CANCELED - Quantification and Calibration of Model-Form and Parameter Uncertainty in Stochastic Dynamics Models with Fractional Derivatives

15:30

A Bayesian Framework for Robust Decisions in the Presence of Misspecified Models

14:00

iCal
Sebastian Krumscheid | RWTH Aachen | Germany

Alex Gorodetsky | University of Michigan | United States

MS681: Multilevel and Multifidelity approaches for forward/inverse Uncertainty Quantification and optimization under uncertainty (Part I of III)

Chair(s)
Panagiotis Tsilifis (EPFL)

Gianluca Geraci (Sandia National Laboratories)

Alex Gorodetsky (University of Michigan)

John Jakeman (Sandia National Laboratories)

Juan Pablo Madrigal Cianci (EPFL)

Michael Eldred (Sandia National Laboratories)

Gianluca Geraci (Sandia National Laboratories)

Alex Gorodetsky (University of Michigan)

John Jakeman (Sandia National Laboratories)

Juan Pablo Madrigal Cianci (EPFL)

Michael Eldred (Sandia National Laboratories)

Room:
Interims Lecture Hall 101

Topic:
Uncertainty propagation

Form of presentation:
Mini-symposium

Duration:
120 Minutes

In the last decades, the advancements in both computer hardware/architectures

and algorithms enabled numerical simulations at unprecedented scales. In parallel,

Uncertainty Quantification (UQ) evolved as a crucial task to enable predictive

numerical simulations. Therefore, a great effort has been devoted in advancing the UQ algorithms

in order to enable UQ for expensive numerical simulations, however the combination of an extremely

large computational cost associated to the evaluation of a high-fidelity model and the presence of a moderate/large

set of uncertainty parameters (often correlated to the complexity of the numerical/physical assumptions)

still represents a formidable challenge for UQ.

Multilevel and multifidelity strategies have been introduced to circumvent these difficulties by

reducing the computational cost required to perform UQ with high-fidelity simulations. The

main idea is to optimally combine simulations of increasingly resolution levels or model fidelities

in order to control the overall accuracy of the surrogates/estimators. This task is accomplished by

combining large number of less accurate numerical simulations with only a limited number of high-fidelity,

numerically expensive, code realizations. In this minisymposium we present contributions related to the state-of-the-art in both forward and inverse multilevel/multifidelity UQ and related areas as optimization under uncertainty.

14:00

Beyond Multilevel Monte Carlo Methods for Expected Values

14:30

Multifidelity uncertainty quantification from a parametric Bayesian viewpoint

14:00

iCal
Raul F. Tempone | RWTH Aachen | Germany

Marc Bocquet | Université Paris-Est | France

Simon Weissmann | University Mannheim | Germany

Matthew E. Levine | California Institute of Technology | United States

MS382: Bridges between Data assimilation and Machine Learning (Part II of II)

Room:
Interims Lecture Hall 102

Topic:
Data assimilation

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Organized in co-operation with

GAMM AG UQ

The task of processing large amounts of data in order to model complex associated dynamical systems is an important challenge of the 21st century. The need for novel mathematical concepts and advanced computational techniques in this context has accelerated research in the associated fields of Data Assimilations and Machine Learning. In recent years the two research communities have been growing closer resulting in advanced numerical methods that combine the strength of both worlds and the development of theoretical underpinning of existing and new techniques. The aim of this MS is to foster these emerging bridges, to detect limitations and possible future alleys by bringing together people from both communities and creating a room for scientific exchange.

14:00

Hierarchical Data Assimilation via Multilevel Monte Carlo

14:30

Data-driven reconstruction of chaotic dynamics using data assimilation and machine learning

15:00

Analysis and application of the ensemble Kalman inversion

15:30

Comparing Frameworks for blending machine learning, physical models, and Data Assimilation Techniques

14:00

iCal
Luc Pronzato | CNRS | France

Motonobu Kanagawa | Eurecom Sophia Antipolis | France

Mark van der Wilk | Imperial College London | United Kingdom

Athénaïs Gautier | Idiap Research Institute and University of Bern | Switzerland

MS232: Incorporating structural information in kernel methods for prediction and design space exploration (Part II of II)

Room:
Exzellenzzentrum 0003

Topic:
Statistical methods for UQ

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Probabilistic and deterministic kernel methods have proven very useful and versatile for a number of classification,

density estimation, and prediction problems arising in science and society. Yet, these methods are often considered as black boxes, and the fantastic expressiveness allowed by the choice of the underlying positive definite kernel is classically underestimated. This double minisymposium gathers researchers from various horizons who have been investigating the incorporation of physical and other structural information in kernel methods in contexts such as Gaussian Process (GP) modelling, adaptive Bayesian integration, space-filling design with minimum energy measures versus maximum mean discrepancy, and probabilistic prediction of probability density fields. In Part I, the emphasis will be put on the incorporation of physical laws and boundary information in GP-related models, with applications in a number of fields encompassing in particular electromagnetism, mechanics, geophysics and biology. In Part II, the focus will be put more specifically on kernels and distances for space-filling design, image-valued GP modelling, high-dimensional integration, and assessing predictions of probability density fields by spatial logistic Gaussian and related models.

14:00

Bounded approximations of singular kernels: minimum energy measures, maximum mean discrepancy and space-filling design

14:30

In High-Dimensional Integration, Structure is Everything

15:00

Learning Invariances using the Marginal Likelihood

15:30

Probabilistic prediction of probability density fields: how to assess and compare predictive performances?

16:15

iCal
Andrey A Popov | Virginia Tech | United States

Oliver Brenner | Institute of Fluid Dynamics, ETH Zurich | Switzerland

Tadeo Javier Cocucci | Universidad Nacional de Córdoba - FaMAF | Argentina

Yannik Behr | GNS Science | New Zealand

Joshua Chen | Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin | United States

Dominic Edelmann | German Cancer Research Center | Germany

Anna Kucerova | Czech Technical University in Prague, Faculty of Civil Engineering | Czech Republic

Ying Liu | The University of Manchester | United Kingdom

Tucker Hartland | University of California Merced | United States

Isaac Sunseri | North Carolina State University | United States

Kellin Rumsey | University of New Mexico | United States

Keyi Wu | University of Texas at Austin | United States

Eliška Janouchová | Czech Technical University in Prague, Faculty of Civil Engineering | Czech Republic

Katrine Bangsgaard | Technical University of Denmark - DTU | Denmark

Victor Churchill | Dartmouth College | United States

Dingcheng Luo | Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin | United States

Jan Sykora | CTU in Prague | Czech Republic

Ki-Tae Kim | University of California, Merced, Applied Mathematics | United States

James Ronan | Dartmouth College | United States

Dingjiong Ma | The University of Hong Kong | Hong Kong

Michael Merritt | NC State University, Department of Mathematics | United States

Zhongjian Wang | The University of Hong Kong | Hong Kong

George Price | The University of Manchester | United Kingdom

Fu Lin | United Technologies Research Center | United States

Albert Lee | Institute for Disease Modeling | United States

Ion Gabriel Ion | TU Darmstadt | Germany

Michael Thomson | University of Nottingham | United Kingdom

Jinwoo Go | Georgia Institute of Technology | United States

Marion Goedel | Technical University of Munich | Germany

Animesh Biswas | University of Kalyani | India

Noémie Le Carrer | Institute for Risk and Uncertainty, University of Liverpool | United Kingdom

Martin Chak | Imperial College London | United Kingdom

Raymond Pang | London School of Economics | United Kingdom

Patrick Héas | INRIA | France

Raul Astudillo | Cornell University | United States

Jeremy Rohmer | BRGM | France

Joscha Reimer | Kiel University | Germany

Kurt Rachares Petvipusit | Equinor ASA | Norway

Ricardo Baptista | MIT | United States

Michel Tosin | Rio de Janeiro State University | Brazil

Jhouben Cuesta Ramirez | CEA LETI & Mines Saint-Etienne | France

Mark Ashworth | Heriot-Watt University | United Kingdom

Daniel Lee | University of Colorado Boulder | United States

Deanna Easley | George Mason University | United States

Julia Konrad | Technical University of Munich | Germany

Bjørn Jensen | Technical University of Denmark - DTU | Denmark

Mona Madlen Dannert | Leibniz Universität Hannover | Germany

Quentin Ayoul-Guilmard | EPFL | Switzerland

Helen Cleaves | North Carolina State University | United States

Alexander Litvinenko | RWTH Aachen | Germany

Walter Arias-Ramirez | University of Maryland, College Park | United States

Nikhil Oberoi | University of Maryland, College Park | United States

Nishan Mudalige | University of Guelph | Canada

Aqeel Afzal Chaudhry | Technische Universität Bergakademie Freiberg | Germany

Brian M. Adams | Sandia National Laboratories | United States

Poster Session

Room:
MW Magistrale/Foyer

Topic:

Form of presentation:
Poster

Duration:
105 Minutes

Important note: ALL poster presentations START at the SAME TIME, at 16:15 on Tuesday afternoon.

Unfortunately, in the "Persons" view of the online conference planner there is a bug, indicating different starting times for each poster. Converia is working to fix the bug, please ignore it in the meantime.

P01: The Multilevel Local Ensemble Transport Kalman Filter

P03: Variational Data Assimilation for Incompressible RANS Closure Models

- CANCELED - P04: Online EM-based parameter estimation for sequential Monte Carlo filtering in data assimilation

P05: Monitoring volcanic lakes using the Unscented Kalman Smoother

P06: A novel Knothe-Rosenblatt Stein variational transport method: applications in data assimilation

P07: Adjusting Simon's two-stage design for uncertainty of the response rate under the null and heterogeneity using historical controls

P08: Three experimental setups for calibration of confined concrete material model

- NEW - P09: Efficient Solvers for Stochastic Galerkin Finite Element Systems Arising in Linear Elasticity Problems

P10: Hierarchical Off-diagonal Low-rank (HODLR) Approximation of Hessians for Inverse Problems with Application to Ice Sheet Flow

P11: A computational framework for quantifying the relative importance of data sources and physical parameters in PDE-based inverse problems

P12: Dealing with measurement uncertainties in Bayesian model calibration

P13: Numerical approximations for Stein variational Newton transport approaches to Bayesian inversion

P14: Probabilistic calibration method for heterogeneous material models

P16: Uncertainty Quantification for Time-Dependent Flat-Field Correction in Absorption Tomography

P17: Identifying damage in sea ice from sparse laser strain measurements

- CANCELED - P18: On Gaussian Mixture Approximations of the Standard Normal: Application to an Advection Diffusion Inverse Problem with Model Uncertainty

P19: Non-intrusive Parameter Identification of Coupled Heat and Moisture Transport

P20: An Extensible Software Framework for Large-Scale Inverse Problems Governed by Partial Differential Equations

P22: Parameter Estimation using Wasserstein distance

P23: A multiscale reduced basis method for Schrodinger equation with multiscale and random potentials

P24: Global Sensitivity Analysis of Chemical Reaction Networks Across Physical Scales

P25: A robust stochastic structure-preserving Lagrangian scheme in computing effective diffusivity of 3D time-dependent flows

P26: Characterising the uncertainty of advection-dominated solute transport in a spatially disordered domain

- CANCELED - P27: Learning Low-Complexity Autoregressive Models via Proximal Alternating Minimization

P28: Evidence-based likelihoods and uncertainties improve calibration and evaluation of mechanistic epidemiological simulations

P29: Physics-Informed Neural Networks for Stochastic High-Frequency Electromagnetics

P30: Parameter estimation for an elastic rod model to determine the mechanical properties of leaves from raster images

P31: Fusing Optimal Uncertainty Quantification with low-rank decomposition techniques

P32: Sensitivity Analysis for Microscopic Crowd Simulation

P33: Uncertainty quantification in contaminated water treatment techniques evaluation using Pythagorean fuzzy TOPSIS through biparametric distance measure

P34: Beyond the probabilistic interpretation of ensemble predictions for nonlinear dynamical systems: Can we go possibilistic?

P35: Generalised Langevin equation with simulated annealing: convergence in probability to global optimum

- CANCELED - P36: Can fire sales risk be assessed based on partial information?

- CANCELED - P37: Generalized Kernel-Based DMD and Non-Linear Reduced Modeling

P39: Bayesian Optimization for Decision Support with Incomplete Risk Preferences

P40: Comparison of different regression techniques for estimating the conditional probability distribution of a censored response - application to marine flooding

P41: Parameter Estimation and Uncertainty Quantification for a Global Marine Biogeochemical Model

- CANCELED - P42: The impact of unexpected drilling events and their corresponding geological uncertainty to reservoir prediction

P43: Machine Learning Uncertainty Representations: modeling random variables via conditional density estimation

P44: A statistical framework to deal with model discrepancy in Zika virus dynamics

P45: Solving inverse problems with multilinear metamodel for costly experiments

- CANCELED - P46: A comparison of random forest and Gaussian process emulator under model uncertainty in reservoir prediction

- CANCELED - P47: Representation, Propagation, and Visualization of Geometric Uncertainty

P48: Generalizing the unscented ensemble transform to higher moments

P49: Multifidelity Monte Carlo Sampling in Plasma Microturbulence Analysis

P50: Acousto-Electric Tomography with uncertain sound speed

P51: Imprecise random field analysis with regard to non-linear material behavior

P52: XMC: a modular Python library for hierarchical Monte Carlo methods in distributed environments

P53: Derivative-Based Global Sensitivity Analysis for Models with High-Dimensional Inputs and Functional Outputs

P54: Uncertainty quantification in the problem of salt contamination of groundwater flow

P55: Towards The Computation Of An Affordable Sensitivity Of A Large Eddy Simulation

P56: Inferring an effective eddy viscosity from High Fidelity Turbulence Data

- CANCELED - P57: BOLD.R: A software package to interface directly with BOLD through R

P58: Local and global sensitivity analyses of thermal consolidation around a point heat source

- CANCELED - P59: Dimension-reduced UQ for Radiation Spectra