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