TODO: here comes the program

07:30 - 08:15

08:15 - 08:30

08:30 - 09:15

Frances Kuo, University of New South Wales

09:15 - 10:00

George Em Karniadakis, Brown University

10:00 - 10:30

10:30 - 12:30

MS161 Probabilistic Numerical Methods for Cubature

MS181 Bifurcations and Uncertainty Quantification (Part I of II)

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

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

MS221 Decision making under uncertainty (Part I of II)

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

MS241 Computer model calibration for large data sets, with applications

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

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

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

MS421 Data Fusion in Remote Sensing

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

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

MS531 Physics Interpretation-Based Uncertainty Quantification

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

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

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

12:30 - 14:00

14:00 - 16:00

MS051 Probabilistic Numerical Methods: Opportunities and Challenges

MS182 Bifurcations and Uncertainty Quantification (Part II of II)

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

MS211 Green Sensitivity: measures from available input/output data

MS222 Decision making under uncertainty (Part II of II)

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

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

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

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

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

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

MS541 Scalable uncertainty quantification in spatial statistics

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

MS631 Uncertainties in multiphase flow models

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

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

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

16:15 - 18:00

08:30 - 10:50

CT01 Bayesian inversion: theory

CT02 Bayesian inversion: applications and software

CT03 Sparse grids and polynomial chaos

CT04 Data assimilation, inverse problems

CT05 UQ in Material Science and Engineering Applications

CT06 UQ in Chemical Engineering and Biology

CT07 UQ in Transport and Energy Engineering Applications

CT08 UQ in Environmental Applications

CT09 Design of Experiments

CT10 Rare Events and Risk

CT11 Optimization

CT12 Multilevel and Multifidelity Monte Carlo

CT13 Gaussian Processes

CT14 Stochastic Dynamics and Multiscale Analysis

CT15 Stochastic Galerkin Methods and Iterative Solvers

CT16 Neural Networks and Machine Learning

CT17 Dimension Reduction

CT18 Model Error and Sensitivity Analysis

CT19 Modeling and Meta-modeling

CT20 ROM and Surrogate Models

CT21 Statistical Methods for UQ

10:50 - 11:15

11:15 - 11:45

Björn Sprungk, TU Bergakademie Freiberg

11:45 - 12:30

David Higdon, Virginia Polytechnic Institute and State University

12:30 - 14:00

14:00 - 16:00

MS021 Probabilistic Numerical Methods for Differential Equations and Linear Algebra (Part I of II)

MS031 Kernel, Quasi-Monte Carlo, and Sparse Grid Methods for High-dimensional Approximation and Integration (Part I of III)

MS061 Uncertainty Quantification in Hydrology (Part I of II)

MS101 Statistical Prediction and Quantification of Extreme Events in Complex Systems (Part I of II)

MS111 Inference and preconditioning via Stein methods, flows, and other transport maps (Part I of II)

MS251 Similarity measures and distances in forward and inverse UQ problems (Part I of II)

MS281 Uncertainty quantification of multi-physics computer models

MS431 Would Hadamard have used Bayes' rule? - On robustness and brittleness of statistical inversion

MS471 Leveraging the Interplay Between UQ and ML for Mutual Benefit (Part I of II)

MS571 Reproducibility and Uncertainty Quantification

MS613 Uncertainty estimates of the cryosphere and its forcings (Part III of III)

MS641 Random PDEs with Lévy Fields

MS661 Gaussian process models and metamodels for non Euclidean inputs (Part I of II)

MS682 Multilevel and Multifidelity approaches for forward/inverse Uncertainty Quantification and optimization under uncertainty (Part II of III)

MS731 Learning Parameters in Complex Physical Systems with Simulation Experiments (Part I of II)

MS791 Bayesian Inference in Earth Science (Part I of II)

MS803 Optimization and Estimation of Complex Systems under Uncertainty Part III of III: Approximation

16:00 - 16:30

16:30 - 18:30

MS022 Probabilistic Numerical Methods for Differential Equations and Linear Algebra (Part II of II)

MS032 Kernel, Quasi-Monte Carlo, and Sparse Grid Methods for High-dimensional Approximation and Integration (Part II of III)

MS062 Uncertainty Quantification in Hydrology (Part II of II)

MS102 Statistical Prediction and Quantification of Extreme Events in Complex Systems (Part II of II)

MS112 Inference and preconditioning via Stein methods, flows, and other transport maps (Part II of II)

MS252 Similarity measures and distances in forward and inverse UQ problems (Part II of II)

MS291 Statistical evaluation and scoring of complex simulations and predictions

MS321 Data Driven Stochastic Optimization

MS331 Recent Advances in Reduced-Order Models for Many Query and Time-Critical Problems

MS472 Leveraging the Interplay Between UQ and ML for Mutual Benefit (Part II of II)

MS591 Bayesian inversions with computationally expensive models

MS621 Computational tools for inverse problems governed by PDEs and UQ

MS662 Gaussian process models and metamodels for non Euclidean inputs (Part II of II)

MS683 Multilevel and Multifidelity approaches for forward/inverse Uncertainty Quantification and optimization under uncertainty (Part III of III)

MS732 Learning Parameters in Complex Physical Systems with Simulation Experiments (Part II of II)

MS781 Adaptive sampling methods for heterogeneous problems

MS792 Bayesian Inference in Earth Science (Part II of II)

18:45 - 19:45

08:30 - 10:30

MS033 Kernel, Quasi-Monte Carlo, and Sparse Grid Methods for High-dimensional Approximation and Integration (Part III of III)

MS131 Robustness analysis of UQ to distribution uncertainty (Part I of II)

MS301 UQ in Finance

MS351 Reduced order methods for uncertainty quantification in CFD parametric problems

MS391 Stochastic processes on large-scale Networks

MS441 Uncertainty Quantification for Earth Remote Sensing (Part I of III)

MS451 Subgrid variability modeling and stochastic parameterization for multiscale uncertainty quantification

MS461 Shape uncertainty quantification and applications (Part I of III)

MS551 Advances in uncertainty quantification for kinetic and transport phenomena (Part I of III)

MS691 Uncertainty Quantification in Deep Learning (Part I of III)

MS711 Inverse modelling using History Matching (Part I of II)

MS721 Optimal Control Methods for Data Assimilation and Simulation

MS741 Machine learning methods for reliability analysis and risk assessment (Part I of II)

MS821 Software for UQ (Part I of III)

MS841 Uncertainty quantification for model complexity reduction

MS851 Computational Uncertainty Quantification for Energy and Power Systems

MS861 Uncertainty in Data and Applications (Part I of II)

10:30 - 11:00

11:00 - 11:45

Anthony Nouy, Centrale Nantes / LMJL

11:45 - 12:30

Elaine Spiller, Marquette University

12:30 - 14:00

14:00 - 16:00

MS011 Recent advances and challenges in optimal experimental design for large-scale inverse problems (Part I of II)

MS091 Algorithms for Large Scale and Non-linear Data Assimilation (Part I of II)

MS121 Computational Statistics meets Computational Dynamics (Part I of II)

MS132 Robustness analysis of UQ to distribution uncertainty (Part II of II)

MS171 UQ for Rough Volatility and Predictive Models in Finance (Part I of II)

MS341 Kernel Methods in Uncertainty Quantification Part I: Kernel-Based Distances, Hypothesis Tests and Statistical Estimators

MS411 Bayesian Inverse Problems and Experimental Design for Complex Systems

MS442 Uncertainty Quantification for Earth Remote Sensing (Part II of III)

MS462 Shape uncertainty quantification and applications (Part II of III)

MS552 Advances in uncertainty quantification for kinetic and transport phenomena (Part II of III)

MS581 Advances in Inverse Problems and Uncertainty Quantification in Cardiovascular Modeling: (Part I of II)

MS671 Achieving a data-model synergy in UQ (Part I of II)

MS692 Uncertainty Quantification in Deep Learning (Part II of III)

MS712 Inverse modelling using History Matching (Part II of II)

MS742 Machine learning methods for reliability analysis and risk assessment (Part II of II)

MS761 Statistical Surrogate Modeling and Optimization for Stochastic Simulation (Part I of II)

MS811 Multilevel and Multi-fidelity Methods for Model-Based Statistical Learning (Part I of III)

MS822 Software for UQ (Part II of III)

MS862 Uncertainty in Data and Applications (Part II of II)

16:00 - 16:30

16:30 - 18:30

MS012 Recent advances and challenges in optimal experimental design for large-scale inverse problems (Part II of II)

MS041 Propagation of uncertainties and parameter inference in material science (Part I of III)

MS092 Algorithms for Large Scale and Non-linear Data Assimilation (Part II of II)

MS122 Computational Statistics meets Computational Dynamics (Part II of II)

MS141 Recent advances in Markov chain Monte Carlo

MS172 UQ for Rough Volatility and Predictive Models in Finance (Part II of II)

MS342 Kernel Methods in Uncertainty Quantification Part II: Monte Carlo Methods and Approximation of Probability Measures

MS443 Uncertainty Quantification for Earth Remote Sensing (Part III of III)

MS463 Shape uncertainty quantification and applications (Part III of III)

MS553 Advances in uncertainty quantification for kinetic and transport phenomena (Part III of III)

MS582 Advances in Inverse Problems and Uncertainty Quantification in Cardiovascular Modeling (Part II of II)

MS651 Tools for enabling Verification, Validation and Uncertainty Quantification in multiscale simulations and workflows

MS672 Achieving a data-model synergy in UQ (Part II of II)

MS693 Uncertainty Quantification in Deep Learning (Part III of III)

MS762 Statistical Surrogate Modeling and Optimization for Stochastic Simulation (Part II of II)

MS812 Multilevel and Multi-fidelity Methods for Model-Based Statistical Learning (Part II of III)

MS823 Software for UQ (Part III of III)

MS871 Characterization and prediction of rare and extreme events in complex systems

18:45 - 19:45

08:30 - 10:30

MS042 Propagation of uncertainties and parameter inference in material science (Part II of III)

MS071 Inverse Problems and Uncertainty Quantification in Biological and Medical Applications (Part I of II)

MS081 Uncertainty Quantification for Data-Intensive Inverse Problems and Learning (Part I of II)

MS151 Uncertainty Quantification and Surrogate Models for Stochastic Simulators (Part I of II)

MS271 The Science of Hazards: Tsunami and Storm Surges

MS361 Theory and simulation of failure probabilities and rare events (Part I of II)

MS371 Data Assimilation: Methods and Applications Earth System Models (Part I of II)

MS401 Recent Advances in Data-driven Modeling for Uncertainty Quantification (Part I of II)

MS481 UQ at the US DOE National Labs (Part I of II)

MS511 UQ for complex fluid dynamics problems in realistic applications (Part I of II)

MS521 Model Uncertainty, Robust Optimization and Predictive Guarantees (Part I of II)

MS601 Deep learning and sparse approximation for high-dimensional problems in uncertainty quantification (Part I of II)

MS751 Advances in likelihood-free inference (Part I of II)

MS771 Ensemble \& Particle methods for inverse problems (Part I of II)

MS813 Multilevel and Multi-fidelity Methods for Model-Based Statistical Learning (Part III of III)

MS831 Stories of Marrying Methods and Applications (Part I of II)

MS881 Recent Advances in Machine Learning and Data-driven Methods for Modeling Uncertainty in Computational Science and Engineering (Part I of II)

MS891 Model-based Optimal Experimental Design (Part I of II)

10:30 - 11:00

11:00 - 11:45

Karen Veroy-Grepl, Eindhoven University of Technology (TU/e)

11:45 - 12:30

Youssef Marzouk, Massachusetts Institute of Technology

12:30 - 14:00

14:00 - 16:00

MS043 Propagation of uncertainties and parameter inference in material science (Part III of III)

MS072 Inverse Problems and Uncertainty Quantification in Biological and Medical Applications (Part II of II)

MS082 Uncertainty Quantification for Data-Intensive Inverse Problems and Learning (Part II of II)

MS152 Uncertainty Quantification and Surrogate Models for Stochastic Simulators (Part II of II)

MS362 Theory and simulation of failure probabilities and rare events (Part II of II)

MS372 Data Assimilation: Methods and Applications Earth System Models (Part II of II)

MS402 Recent Advances in Data-driven Modeling for Uncertainty Quantification (Part II of II)

MS482 UQ at the US DOE National Labs (Part II of II)

MS512 UQ for complex fluid dynamics problems in realistic applications (Part II of II)

MS522 Model Uncertainty, Robust Optimization and Predictive Guarantees (Part II of II)

MS561 The OpenTURNS software for Uncertainty Quantification

MS602 Deep learning and sparse approximation for high-dimensional problems in uncertainty quantification (Part II of II)

MS752 Advances in likelihood-free inference (Part II of II)

MS772 Ensemble \& Particle methods for inverse problems (Part II of II)

MS832 Stories of Marrying Methods and Applications (Part II of II)

MS882 Recent Advances in Machine Learning and Data-driven Methods for Modeling Uncertainty in Computational Science and Engineering (Part II of II)

MS892 Model-based Optimal Experimental Design (Part II of II)

### Tuesday, March 24

07:30 - 08:15

08:15 - 08:30

08:30 - 09:15

**IP01 New Quasi-Monte Carlo Strategies for UQ**Frances Kuo, University of New South Wales

09:15 - 10:00

**IP02 Physics-Informed Neural Networks (PINNs) with Uncertainty Quantification**George Em Karniadakis, Brown University

10:00 - 10:30

10:30 - 12:30

#### Concurrent Sessions

MS161 Probabilistic Numerical Methods for Cubature

MS181 Bifurcations and Uncertainty Quantification (Part I of II)

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

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

MS221 Decision making under uncertainty (Part I of II)

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

MS241 Computer model calibration for large data sets, with applications

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

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

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

MS421 Data Fusion in Remote Sensing

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

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

MS531 Physics Interpretation-Based Uncertainty Quantification

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

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

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

**MT01 Data assimilation and ensemble forecasting in meteorology,**Tijana Janjic Pfander, University of Munich12:30 - 14:00

14:00 - 16:00

#### Concurrent Sessions

MS051 Probabilistic Numerical Methods: Opportunities and Challenges

MS182 Bifurcations and Uncertainty Quantification (Part II of II)

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

MS211 Green Sensitivity: measures from available input/output data

MS222 Decision making under uncertainty (Part II of II)

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

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

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

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

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

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

MS541 Scalable uncertainty quantification in spatial statistics

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

MS631 Uncertainties in multiphase flow models

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

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

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

**MT02 Introduction to Experimental Design,**Max Morris, Iowa State University16:15 - 18:00

#### Poster Session

### Wednesday, March 25

08:30 - 10:50

#### Concurrent Sessions

CT01 Bayesian inversion: theory

CT02 Bayesian inversion: applications and software

CT03 Sparse grids and polynomial chaos

CT04 Data assimilation, inverse problems

CT05 UQ in Material Science and Engineering Applications

CT06 UQ in Chemical Engineering and Biology

CT07 UQ in Transport and Energy Engineering Applications

CT08 UQ in Environmental Applications

CT09 Design of Experiments

CT10 Rare Events and Risk

CT11 Optimization

CT12 Multilevel and Multifidelity Monte Carlo

CT13 Gaussian Processes

CT14 Stochastic Dynamics and Multiscale Analysis

CT15 Stochastic Galerkin Methods and Iterative Solvers

CT16 Neural Networks and Machine Learning

CT17 Dimension Reduction

CT18 Model Error and Sensitivity Analysis

CT19 Modeling and Meta-modeling

CT20 ROM and Surrogate Models

CT21 Statistical Methods for UQ

10:50 - 11:15

11:15 - 11:45

**IP SIAG/UQ Early Career Prize**Björn Sprungk, TU Bergakademie Freiberg

11:45 - 12:30

**IP03 UQ and Bayesian Model Calibration Applied to Stochastic Systems**David Higdon, Virginia Polytechnic Institute and State University

12:30 - 14:00

14:00 - 16:00

#### Concurrent Sessions

MS021 Probabilistic Numerical Methods for Differential Equations and Linear Algebra (Part I of II)

MS031 Kernel, Quasi-Monte Carlo, and Sparse Grid Methods for High-dimensional Approximation and Integration (Part I of III)

MS061 Uncertainty Quantification in Hydrology (Part I of II)

MS101 Statistical Prediction and Quantification of Extreme Events in Complex Systems (Part I of II)

MS111 Inference and preconditioning via Stein methods, flows, and other transport maps (Part I of II)

MS251 Similarity measures and distances in forward and inverse UQ problems (Part I of II)

MS281 Uncertainty quantification of multi-physics computer models

MS431 Would Hadamard have used Bayes' rule? - On robustness and brittleness of statistical inversion

MS471 Leveraging the Interplay Between UQ and ML for Mutual Benefit (Part I of II)

MS571 Reproducibility and Uncertainty Quantification

MS613 Uncertainty estimates of the cryosphere and its forcings (Part III of III)

MS641 Random PDEs with Lévy Fields

MS661 Gaussian process models and metamodels for non Euclidean inputs (Part I of II)

MS682 Multilevel and Multifidelity approaches for forward/inverse Uncertainty Quantification and optimization under uncertainty (Part II of III)

MS731 Learning Parameters in Complex Physical Systems with Simulation Experiments (Part I of II)

MS791 Bayesian Inference in Earth Science (Part I of II)

MS803 Optimization and Estimation of Complex Systems under Uncertainty Part III of III: Approximation

**MT03 Practical approximation in high dimensions,**Ben Adcock, Simon Fraser University16:00 - 16:30

16:30 - 18:30

#### Concurrent Sessions

MS022 Probabilistic Numerical Methods for Differential Equations and Linear Algebra (Part II of II)

MS032 Kernel, Quasi-Monte Carlo, and Sparse Grid Methods for High-dimensional Approximation and Integration (Part II of III)

MS062 Uncertainty Quantification in Hydrology (Part II of II)

MS102 Statistical Prediction and Quantification of Extreme Events in Complex Systems (Part II of II)

MS112 Inference and preconditioning via Stein methods, flows, and other transport maps (Part II of II)

MS252 Similarity measures and distances in forward and inverse UQ problems (Part II of II)

MS291 Statistical evaluation and scoring of complex simulations and predictions

MS321 Data Driven Stochastic Optimization

MS331 Recent Advances in Reduced-Order Models for Many Query and Time-Critical Problems

MS472 Leveraging the Interplay Between UQ and ML for Mutual Benefit (Part II of II)

MS591 Bayesian inversions with computationally expensive models

MS621 Computational tools for inverse problems governed by PDEs and UQ

MS662 Gaussian process models and metamodels for non Euclidean inputs (Part II of II)

MS683 Multilevel and Multifidelity approaches for forward/inverse Uncertainty Quantification and optimization under uncertainty (Part III of III)

MS732 Learning Parameters in Complex Physical Systems with Simulation Experiments (Part II of II)

MS781 Adaptive sampling methods for heterogeneous problems

MS792 Bayesian Inference in Earth Science (Part II of II)

**MT04 Can we make long-term predictions?,**Michela Ottobre, Heriot-Watt University18:45 - 19:45

### Thursday, March 26

08:30 - 10:30

#### Concurrent Sessions

MS033 Kernel, Quasi-Monte Carlo, and Sparse Grid Methods for High-dimensional Approximation and Integration (Part III of III)

MS131 Robustness analysis of UQ to distribution uncertainty (Part I of II)

MS301 UQ in Finance

MS351 Reduced order methods for uncertainty quantification in CFD parametric problems

MS391 Stochastic processes on large-scale Networks

MS441 Uncertainty Quantification for Earth Remote Sensing (Part I of III)

MS451 Subgrid variability modeling and stochastic parameterization for multiscale uncertainty quantification

MS461 Shape uncertainty quantification and applications (Part I of III)

MS551 Advances in uncertainty quantification for kinetic and transport phenomena (Part I of III)

MS691 Uncertainty Quantification in Deep Learning (Part I of III)

MS711 Inverse modelling using History Matching (Part I of II)

MS721 Optimal Control Methods for Data Assimilation and Simulation

MS741 Machine learning methods for reliability analysis and risk assessment (Part I of II)

MS821 Software for UQ (Part I of III)

MS841 Uncertainty quantification for model complexity reduction

MS851 Computational Uncertainty Quantification for Energy and Power Systems

MS861 Uncertainty in Data and Applications (Part I of II)

**MT05 Spatial statistics,**Thordis Thorarinsdottir, Norwegian Computing Center**MT06 Multi-fidelity UQ,**Michael Eldred, Sandia National Laboratories10:30 - 11:00

11:00 - 11:45

**IP04 Approximation and Learning with Tree Tensor Networks**Anthony Nouy, Centrale Nantes / LMJL

11:45 - 12:30

**IP05 Assessing and forecasting hazards in an uncertain future**Elaine Spiller, Marquette University

12:30 - 14:00

14:00 - 16:00

#### Concurrent Sessions

MS011 Recent advances and challenges in optimal experimental design for large-scale inverse problems (Part I of II)

MS091 Algorithms for Large Scale and Non-linear Data Assimilation (Part I of II)

MS121 Computational Statistics meets Computational Dynamics (Part I of II)

MS132 Robustness analysis of UQ to distribution uncertainty (Part II of II)

MS171 UQ for Rough Volatility and Predictive Models in Finance (Part I of II)

MS341 Kernel Methods in Uncertainty Quantification Part I: Kernel-Based Distances, Hypothesis Tests and Statistical Estimators

MS411 Bayesian Inverse Problems and Experimental Design for Complex Systems

MS442 Uncertainty Quantification for Earth Remote Sensing (Part II of III)

MS462 Shape uncertainty quantification and applications (Part II of III)

MS552 Advances in uncertainty quantification for kinetic and transport phenomena (Part II of III)

MS581 Advances in Inverse Problems and Uncertainty Quantification in Cardiovascular Modeling: (Part I of II)

MS671 Achieving a data-model synergy in UQ (Part I of II)

MS692 Uncertainty Quantification in Deep Learning (Part II of III)

MS712 Inverse modelling using History Matching (Part II of II)

MS742 Machine learning methods for reliability analysis and risk assessment (Part II of II)

MS761 Statistical Surrogate Modeling and Optimization for Stochastic Simulation (Part I of II)

MS811 Multilevel and Multi-fidelity Methods for Model-Based Statistical Learning (Part I of III)

MS822 Software for UQ (Part II of III)

MS862 Uncertainty in Data and Applications (Part II of II)

16:00 - 16:30

16:30 - 18:30

#### Concurrent Sessions

MS012 Recent advances and challenges in optimal experimental design for large-scale inverse problems (Part II of II)

MS041 Propagation of uncertainties and parameter inference in material science (Part I of III)

MS092 Algorithms for Large Scale and Non-linear Data Assimilation (Part II of II)

MS122 Computational Statistics meets Computational Dynamics (Part II of II)

MS141 Recent advances in Markov chain Monte Carlo

MS172 UQ for Rough Volatility and Predictive Models in Finance (Part II of II)

MS342 Kernel Methods in Uncertainty Quantification Part II: Monte Carlo Methods and Approximation of Probability Measures

MS443 Uncertainty Quantification for Earth Remote Sensing (Part III of III)

MS463 Shape uncertainty quantification and applications (Part III of III)

MS553 Advances in uncertainty quantification for kinetic and transport phenomena (Part III of III)

MS582 Advances in Inverse Problems and Uncertainty Quantification in Cardiovascular Modeling (Part II of II)

MS651 Tools for enabling Verification, Validation and Uncertainty Quantification in multiscale simulations and workflows

MS672 Achieving a data-model synergy in UQ (Part II of II)

MS693 Uncertainty Quantification in Deep Learning (Part III of III)

MS762 Statistical Surrogate Modeling and Optimization for Stochastic Simulation (Part II of II)

MS812 Multilevel and Multi-fidelity Methods for Model-Based Statistical Learning (Part II of III)

MS823 Software for UQ (Part III of III)

MS871 Characterization and prediction of rare and extreme events in complex systems

18:45 - 19:45

### Friday, March 27

08:30 - 10:30

#### Concurrent Sessions

MS042 Propagation of uncertainties and parameter inference in material science (Part II of III)

MS071 Inverse Problems and Uncertainty Quantification in Biological and Medical Applications (Part I of II)

MS081 Uncertainty Quantification for Data-Intensive Inverse Problems and Learning (Part I of II)

MS151 Uncertainty Quantification and Surrogate Models for Stochastic Simulators (Part I of II)

MS271 The Science of Hazards: Tsunami and Storm Surges

MS361 Theory and simulation of failure probabilities and rare events (Part I of II)

MS371 Data Assimilation: Methods and Applications Earth System Models (Part I of II)

MS401 Recent Advances in Data-driven Modeling for Uncertainty Quantification (Part I of II)

MS481 UQ at the US DOE National Labs (Part I of II)

MS511 UQ for complex fluid dynamics problems in realistic applications (Part I of II)

MS521 Model Uncertainty, Robust Optimization and Predictive Guarantees (Part I of II)

MS601 Deep learning and sparse approximation for high-dimensional problems in uncertainty quantification (Part I of II)

MS751 Advances in likelihood-free inference (Part I of II)

MS771 Ensemble \& Particle methods for inverse problems (Part I of II)

MS813 Multilevel and Multi-fidelity Methods for Model-Based Statistical Learning (Part III of III)

MS831 Stories of Marrying Methods and Applications (Part I of II)

MS881 Recent Advances in Machine Learning and Data-driven Methods for Modeling Uncertainty in Computational Science and Engineering (Part I of II)

MS891 Model-based Optimal Experimental Design (Part I of II)

10:30 - 11:00

11:00 - 11:45

**IP06 Optimal experimental design for the quantification of model uncertainty: A functional analysis perspective**Karen Veroy-Grepl, Eindhoven University of Technology (TU/e)

11:45 - 12:30

**IP07 Transport methods for stochastic modeling and inference**Youssef Marzouk, Massachusetts Institute of Technology

12:30 - 14:00

14:00 - 16:00

#### Concurrent Sessions

MS043 Propagation of uncertainties and parameter inference in material science (Part III of III)

MS072 Inverse Problems and Uncertainty Quantification in Biological and Medical Applications (Part II of II)

MS082 Uncertainty Quantification for Data-Intensive Inverse Problems and Learning (Part II of II)

MS152 Uncertainty Quantification and Surrogate Models for Stochastic Simulators (Part II of II)

MS362 Theory and simulation of failure probabilities and rare events (Part II of II)

MS372 Data Assimilation: Methods and Applications Earth System Models (Part II of II)

MS402 Recent Advances in Data-driven Modeling for Uncertainty Quantification (Part II of II)

MS482 UQ at the US DOE National Labs (Part II of II)

MS512 UQ for complex fluid dynamics problems in realistic applications (Part II of II)

MS522 Model Uncertainty, Robust Optimization and Predictive Guarantees (Part II of II)

MS561 The OpenTURNS software for Uncertainty Quantification

MS602 Deep learning and sparse approximation for high-dimensional problems in uncertainty quantification (Part II of II)

MS752 Advances in likelihood-free inference (Part II of II)

MS772 Ensemble \& Particle methods for inverse problems (Part II of II)

MS832 Stories of Marrying Methods and Applications (Part II of II)

MS882 Recent Advances in Machine Learning and Data-driven Methods for Modeling Uncertainty in Computational Science and Engineering (Part II of II)

MS892 Model-based Optimal Experimental Design (Part II of II)