A physics-informed operator regression framework for extracting data-driven continuum models

Patel, Ravi Ghanshyam; Trask, Nathaniel Albert; Wood, Mitchell A.; Cyr, Eric C.

doi:10.1016/j.cma.2020.113500

A physics-informed operator regression framework for extracting data-driven continuum models

Journal Article · Thu Nov 05 00:00:00 EST 2020 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2020.113500· OSTI ID:1725869

Patel, Ravi Ghanshyam ^[1]; Trask, Nathaniel Albert ^[1]; Wood, Mitchell A. ^[1]; Cyr, Eric C. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. Here, we demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1725869

Alternate ID(s):: OSTI ID: 1776289

Report Number(s):: SAND--2020-5359J; 686274

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Vol. 373; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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continuum scale modeling
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A physics-informed operator regression framework for extracting data-driven continuum models

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