TODO: here comes the program

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 Munich

12:30 - 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 University

16:15 - 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 University

16:00 - 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 University

18:45 - 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 Laboratories

10:30 - 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)