Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Alber, Mark; Buganza Tepole, Adrian; Cannon, William R.; De, Suvranu; Dura-Bernal, Salvador; Garikipati, Krishna; Karniadakis, George E.; Lytton, William W.; Kuhl, Ellen; Petzold, Linda

doi:10.1038/s41746-019-0193-y

Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Journal Article · Mon Nov 25 00:00:00 EST 2019 · npj Digital Medicine

DOI:https://doi.org/10.1038/s41746-019-0193-y· OSTI ID:1597499

Alber, Mark ^[1]; Buganza Tepole, Adrian ^[2]; ^[3]; De, Suvranu ^[4]; Dura-Bernal, Salvador ^[5]; Garikipati, Krishna ^[6]; Karniadakis, George E. ^[7]; Lytton, William W. ^[5]; Kuhl, Ellen ^[8]; Petzold, Linda ^[9]

Univ. of California, Riverside, CA (United States)
Purdue Univ., West Lafayette, IN (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Rensselaer Polytechnic Inst., Troy, NY (United States)
State Univ. of New York (SUNY), Brooklyn, NY (United States). Downstate Medical Center and Kings County Hospital
Univ. of Michigan, Ann Arbor, MI (United States)
Brown Univ., Providence, RI (United States)
Stanford Univ., CA (United States)
Univ. of California, Santa Barbara, CA (United States)

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunityin this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1597499

Report Number(s):: PNNL-SA--147139

Journal Information:: npj Digital Medicine, Journal Name: npj Digital Medicine Journal Issue: 1 Vol. 2; ISSN 2398-6352

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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