Speakers

Invited speakers

Erin Acquesta, Sandia National Laboratories
Adapting Verification and Validation Principles to a Credibility Process for Scientific Machine Learning

Tan Bui-Thanh, The University of Texas at Austin
Model-Constrained Deep Learning Methods for Forward and Inverse Problems

Varun Chandola, University at Buffalo, National Science Foundation
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds

Wei Chen, Northwestern University
Physics-Informed Latent Variable Gaussian Process Modeling and Uncertainty Quantification for Adaptive Discovery and Design

Zhuotong Chen, University of California, Santa Barbara
Self-Healing Robust Neural Networks via Closed-Loop Control

Americo Cunha Jr., Rio de Janeiro State University
A Cross-Entropy Approximate Bayesian Computation Framework for Uncertainty Quantification on Mechanistic Epidemic Models

Marta D'Elia, Meta Reality Labs
A Data-Driven Continuum Model for Upscaling Noisy Molecular Dynamics Displacements

Alireza Doostan, University of Colorado Boulder
Recovering Dynamical Systems from Noisy Measurements Using Constrained Optimization

Krishna Garikipati, University of Michigan
Bayesian Neural Networks for Weak Solution of PDEs with Uncertainty Quantification

Roger Ghanem, University of Southern California
Model Refinement as Probabilistic Learning

Alex Gorodetsky, University of Michigan
Uncertainty Quantification with Multi-Fidelity Networks

Gianluca Iaccarino, Stanford University
Uncertainty Quantification in Turbulence Modeling

Vipin Kumar, University of Minnesota
Knowledge-Guided Machine Learning (KGML): A New Framework for Accelerating Scientific Discovery

Youssef Marzouk, Massachusetts Institute of Technology
Likelihood-Free Bayesian Inference via Transportation of Measure

Romit Maulik, Argonne National Laboratory
Quantifying Uncertainty in Deep Learning for Fluid Flow Reconstruction

Kathryn Maupin, Sandia National Laboratories
Informing Missing Physics with Model Form Error and Model Selection

Dhruv Patel, Stanford University
Multifidelity Monte-Carlo Sampling in Mechanics

Deep Ray, University of Southern California
Conditional GANs and Their Generalizability in Physics-Based Inverse Problems

Cosmin Safta, Sandia National Laboratories
Graph Convolutional Neural Networks for Modeling Materials with Microstructure

Daniele Schiavazzi, University of Notre Dame
Adaptive and Privacy Preserving Inference for Physics Based Models

Michael Shields, Johns Hopkins University
Manifold Learning for High-Dimensional Uncertainty Quantification

Christian Soize, Université Gustave Eiffel
Posterior Probabilistic Learning Constrained by Stochastic PDE and Experimental Statistical Moments of Physics Observations

Nathaniel Trask, Sandia National Laboratories
POU-nets: Physics-Informed ML, Structured Preservation, and Multimodal Data

Jian-xun Wang, University of Notre Dame
Predicting Parametric Spatiotemporal Dynamics by Multi-Resolution PDE Structure-Preserved Deep Learning

Yue Yu, Lehigh University
Bayesian Nonlocal Operator Regression (BNOR): Towards the Characterization of Uncertainty in Heterogeneous Materials

Zongren Zou, Brown University
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons