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

By Mark S. Alber, A. Buganza Tepole, William R. Cannon, Suvranu De, Savador Dura-Bernal, Krishna C. Garikipati, G. Em Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold and 1 more. 2019

551 citations recorded by the source corpus.

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 opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can 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 datasets 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 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.

Subfields: Biophysics, Modeling and Simulation, Statistical and Nonlinear Physics

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Citations in the corpus

Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, background): “This powerful framework allows to train a neural network that simultaneously approximates the data and conforms to the partial diﬀerential equations that represents the physical knowledge of the system [14], a concept known as physics-informed neural networks.”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “…approach can be considered sufficient for complex scientific and engineering applications, the research community is beginning to explore the continuum between mechanistic and ML models, where both scientific knowledge and data are integrated in a synergistic manner [2, 14, 126, 204].”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “, earth systems [232], climate science [87, 153, 207], turbulence modeling [37, 199, 295], computational physics [261], cyberphysical systems [223], material discovery [49, 222, 243], quantum chemistry [240, 245], biological sciences [8, 215, 306], and hydrology [275, 297]), and it is being performe”
The ‘Digital Twin’ to enable the vision of precision cardiology (2020, unknown): “7 This borrows but expands the concept of ‘digital twin’ used in engineering industries, where in silico representations of a physical system, such as an engine or a wind farm, are used to optimize design or control processes, with a real-time connection between the physical system and the model.”