Multiscale modeling meets machine learning: What can we learn?

Archives of Computational Methods in Engineering : State of the Art Reviews
Grace C Y PengEllen Kuhl

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 mach...Continue Reading

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Citations

Aug 25, 2020·Computational Mechanics·Kevin LinkaEllen Kuhl
Mar 17, 2021·The Journal of Chemical Physics·Chensen LinGeorge Em Karniadakis
Sep 10, 2020·Computational Mechanics·Kevin LinkaEllen Kuhl
Nov 11, 2020·Briefings in Bioinformatics·Giulia MuzioKarsten Borgwardt
May 11, 2021·Current Pathobiology Reports·Lukasz BurzawaDavid M Umulis
Jul 24, 2021·Soft Matter·Rasul AbdusalamovAmeya Rege

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