Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations

Harsh Bhatia, Timothy S. Carpenter, Helgi I. Ingólfsson, Gautham Dharuman, Piyush Karande, Shusen Liu, Tomas Oppelstrup, Chris Neale, Felice C. Lightstone, Brian Van Essen, James N. Glosli, Peer Timo Bremer

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Multiscale simulations are a well-accepted way to bridge the length and time scales required for scientific studies with the solution accuracy achievable through available computational resources. Traditional approaches either solve a coarse model with selective refinement or coerce a detailed model into faster sampling, both of which have limitations. Here, we present a paradigm of adaptive, multiscale simulations that couple different scales using a dynamic-importance sampling approach. Our method uses machine learning to dynamically and exhaustively sample the phase space explored by a macro model using microscale simulations and enables an automatic feedback from the micro to the macro scale, leading to a self-healing multiscale simulation. As a result, our approach delivers macro length and time scales, but with the effective precision of the micro scale. Our approach is arbitrarily scalable as well as transferable to many different types of simulations. Our method made possible a multiscale scientific campaign of unprecedented scale to understand the interactions of RAS proteins with a plasma membrane in the context of cancer research running over several days on Sierra, which is currently the second-most-powerful supercomputer in the world.

Original languageEnglish (US)
Pages (from-to)401-409
Number of pages9
JournalNature Machine Intelligence
Volume3
Issue number5
DOIs
StatePublished - May 2021
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Artificial Intelligence

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