Romit Maulik, Argonne National Laboratory
Qi Tang, Los Alamos National Laboratory
Joshua Burby, Los Alamos National Laboratory
Machine learning techniques have recently shown remarkable results for tackling long-standing challenges in computational science. This minisymposium seeks to showcase recent advances in data-driven modeling for dramatically accelerating computational applications with particular emphasis on physics-informed approaches. We are interested in strategies that can leverage large, potentially real-world data sets for learning structure preserving dynamical systems, developing physics-informed closures for advection-dominated problems, and building hybrid PDE and data-driven modeling approaches that significantly accelerate computational workflows for diverse applications.
Confirmed speakers:
Shivam Barwey - Argonne National Laboratory
Stephen Becker - University of Colorado Boulder
David Bortz - University of Colorado Boulder
Andrew Christlieb - Michigan State University
Yingda Cheng - Michigan State University
Emil Constantinescu - Argonne National Laboratory
Cory Hauck - Oak Ridge National Laboratory
Huan Lei - Michigan State University
Daniel Livescu - Los Alamos National Laboratory
Gianmarco Mengaldo - National University of Singapore
Houman Owhadi - Caltech
Daniel Serino - Los Alamos National Laboratory
Xuping Xie - Los Alamos National Laboratory
