Webinar Speakers

2022

April 11, 2022	Houman Owhadi CalTech "Computational Graph Completion"
April 4, 2022	Laure Zanna New York University "Machine Learning for Ocean and Climate Modeling: Advances & Challenges"
March 14, 2022	Mengjia Xu McGovern Institute for Brain Research, Center for Brains, Minds and Machines, MIT and Brown University "Graph Embedding with Uncertainty Quantification for Diverse Applications"
March 7, 2022	Priya Panda Yale University "Exploring Robustness in Neural Systems with Spike-based Machine Intelligence"
February 28, 2022	Lai-Yung (Ruby) Leung Pacific Northwest National Laboratory "Earth System Modeling for Actionable Science"
February 21, 2022	Kara Peterson Sandia National Laboratories "Analytics for Climate Prediction and Attribution"
February 14, 2022	Mamikon Gulian Sandia National Laboratories "Connections Between Nonlocal Operators: From Vector Calculus Identities to a Fractional Helmholtz Decomposition"
January 31, 2022	Jonas Landman QC Ware & The University of Edinburgh "Quantum Algorithms and Machine Learning"

2021

December 6, 2021	Grace Gu University of California, Berkeley "Generative Design and Additive Manufacturing of Three-Dimensional Architected Metamaterials"
November 15, 2021	Brad Aimone Sandia National Laboratories "Recipes and Tools for Neuromorphic Computing"
August 30, 2021	Remi Dingreville Sandia National Laboratories "Accelerating phase-field predictions of physical vapor deposition using machine-learning methods: A computational material platform for understanding and designing functional nanostructured thin-films"
August 16, 2021	Youngsoo Choi Lawrence Livermore National Laboratory "Physics-constrained data-driven methods of accurately accelerating simulations and their applications"
July 12, 2021	Marta D'Elia Sandia National Laboratories "Data-driven learning of nonlocal models: bridging scales and design of new neural networks"
June 14, 2021	Mark Ainsworth Brown University "Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control"
June 7, 2021	Nat Trask Sandia National Laboratories “Structure preserving architectures for SciML”
May 10, 2021	Paul Atzberger University of California, Santa Barbara "Machine Learning for Investigating Dynamics of Physical Systems"
May 4, 2021	Greg Valiant Stanford University "Calibration, Mis-Specification, and Selective Learning"
April 26, 2021	Costis Daskalakis Massachusetts Institute of Technology "Equilibrium Computation and the Foundations of Deep Learning"
April 5, 2021	Mihai Anitescu Argonne National Laboratory "Power System Thermodynamics"
March 22, 2021	Samuel Lanthaler ETH Zurich "Error Estimates for DeepOnets"
February 22, 2021	Henry Abarbanel University of California, San Diego "How Reservoir Computing Works to Learn and Forecast Dynamical Data"
January 18, 2021	Andrew Stuart California Institute of Technology "Learning Operators- Supervised Learning Between Banach Spaces"

2020

December 14, 2020	Kevin Carlberg University of Washington "Nonlinear model reduction: using machine learning to enable rapid simulation of extreme-scale physics models"
November 23, 2020	Kenneth Golden University of Utah "Modeling Sea Ice as a Multiscale Composite Material"
November 16, 2020	Patrick Kidger University of Oxford "Neural Controlled Differential Equations: continuous RNNs, irregular time series, GANs"
September 28, 2020	Dirk Hartmann Senior Principal Scientist, Siemens "Mathematics a key enabler for Digital Twins"
September 21, 2020	Eric Vander-Eijnden New York University "Machine learning and PDEs"
August 17, 2020	Mamikon Gulian Sandia National Laboratories "A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges"
August 3, 2020	Chinmay Hedge New York University "Untrained Neural Priors: Theory and Applications to PDEs"
July 13, 2020	Bethany Lusch Argonne National Laboratory "Scientific Machine Learning at the Argonne Leadership Computing Facility"
July 6, 2020	Professor Christoph Schwab ETH-Zurich University "Numerical Analysis of Deep Neural Networks for PDEs"
June 29, 2020	Pavel Bochev Sandia National Laboratories "Data-driven models for photocurrent effects in semiconductor devices"
June 22, 2020	Eric Cyr Sandia National Laboratories "A Layer-Parallel Approach for Training Deep Neural Networks"
June 8, 2020	Lars Ruthotto Emory University "Machine Learning meets Optimal Transport: Old solutions for new problems and vice versa"
June 1, 2020	Wei Zhu Duke University "Applied differential geometry and harmonic analysis in deep learning regularization"
May 18, 2020	Aidan Thompson Sandia National Laboratories "Predictive Atomistic Simulations of Materials using SNAP Data-Driven Potentials"
May 11, 2020	Hrushikesh Mhaskar Claremont Graduate University "Learning with an asymptotically optimal number of samples"
May 4, 2020	Yeonjong Shin Brown University "On the Convergence and Generalization of Physics Informed Neural Networks"
April 27, 2020	Eric Darve and Kailai Xu Stanford University "Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning"
April 27, 2020	Qizhi He Pacific Northwest National Laboratory "Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport"
April 13, 2020	Nat Trask Sandia National Laboratories "Physics informed graph neural networks: a unification of PINNs with mimetic PDE discretizations"
February 10, 2020	Celia Reina University of Pennsylvania "Harnessing Fluctuations to Discover Dissipative Evoluation Equations"

PNNL

Physics-Informed Learning Machines for Multiscale and Multiphysics Problems

Webinar Speakers

2022

2021

2020