Multiscale modeling meets machine learning: What can we learn?

Journal: Archives of computational methods in engineering : state of the art reviews
Published Date: Feb 17, 2020

Abstract

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

Authors

  • Grace C Y Peng
    National Institutes of Health, Bethesda, Maryland, USA.
  • Mark Alber
    University of California, Riverside, USA.
  • Adrian Buganza Tepole
    School of Mechanical Engineering, Purdue University, West Lafayette, USA.
  • William R Cannon
    Pacific Northwest National Laboratory, Richland, Washington, USA.
  • Suvranu De
    Rensselaer Polytechnic Institute, Troy, New York, USA.
  • Salvador Dura-Bernal
    State University of New York, New York, USA.
  • Krishna Garikipati
    University of Michigan Ann Arbor, Michigan, USA.
  • George Karniadakis
    Brown University, Providence, Rhode Island, USA.
  • William W Lytton
    Department of Neurology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States.
  • Paris Perdikaris
    University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Linda Petzold
    University of California, Santa Barbara, California, USA.
  • Ellen Kuhl
    Department of Mechanical Engineering, Stanford University, Stanford, USA.

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