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Machine learning is increasingly recognized as a promising technology in thebiological, biomedical, and behavioral sciences. There can be no argument thatthis technique is incredibly successful in image recognition with immediateapplications in diagnostics including electrophysiology, radiology, orpathology, where we have access to massive amounts of annotated data. However,machine learning often performs poorly in prognosis, especially when dealingwith sparse data. This is a field where classical physics-based simulationseems to remain irreplaceable. In this review, we identify areas in thebiomedical sciences where machine learning and multiscale modeling can mutuallybenefit from one another: Machine learning can integrate physics-basedknowledge in the form of governing equations, boundary conditions, orconstraints to manage ill-posted problems and robustly handle sparse and noisydata; multiscale modeling can integrate machine learning to create surrogatemodels, identify system dynamics and parameters, analyze sensitivities, andquantify uncertainty to bridge the scales and understand the emergence offunction. With a view towards applications in the life sciences, we discuss thestate of the art of combining machine learning and multiscale modeling,identify applications and opportunities, raise open questions, and addresspotential challenges and limitations. We anticipate that it will stimulatediscussion within the community of computational mechanics and reach out toother disciplines including mathematics, statistics, computer science,artificial intelligence, biomedicine, systems biology, and precision medicineto join forces towards creating robust and efficient models for biologicalsystems.

Multiscale modeling meets machine learning: What can we learn?