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Data-driven Modeling and Physics-based Learning in the Biomedical Sciences

Ellen Kuhl, Stanford University

The biomedical sciences are collecting more data than ever before, and there is a critical need efficiently screen, analyze, and interpret these data with a view to improve human health. Machine learning is increasingly recognized as a promising strategy to explore big biomedical data; but machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Physics-based modeling can integrate multiscale, multiphysics data and explain the emergence of function, but physics-based modeling alone is poorly suited to combine large datasets from different sources and different levels of resolution. The tight integration of data-driven modeling and physics-based learning offers the potential to integrate multimodality data, uncover mechanisms, and predict system dynamics across the scales and fields. Here we demonstrate the application of data-driven modeling to different biomedical systems including the heart and the brain. We illustrate the use of physics-based learning to infer model parameters and quantify uncertainty using biomedical data from multiple sources and scales. Typical applications range from drug safety evaluation, heart rhythm disorders, and heart failure to neurodevelopment, traumatic brain injury, and dementia. We highlight modeling opportunities, address open questions, and discuss potential challenges and limitations. We anticipate that these examples will stimulate discussion how to efficiently and robustly integrate machine learning and physics-based modeling to provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform clinical decision making in the benefit of human health.


Ellen Kuhl is the Walter B. Reinhold Professor in the School of Engineering and Robert Bosch Chair of Mechanical Engineering at Stanford University. She is a Professor of Mechanical Engineering and, by courtesy, Bioengineering. She received her PhD from the University of Stuttgart in 2000 and her Habilitation from the University of Kaiserslautern in 2004. Her area of expertise is Living Matter Physics, the design of theoretical and computational models to simulate and predict the behavior of living structures. Ellen has published more than 250 peer-reviewed journal articles and written and edited three books; she is an active reviewer for more than 20 journals at the interface of engineering and medicine and an editorial board member of eight international journals in her field. She is a founding member of the Living Heart Project, a translational research initiative to revolutionize cardiovascular science through realistic simulation with 400 participants from research, industry, and medicine from 24 countries. Ellen is the current Chair of the US National Committee on Biomechanics and a Member-Elect of the World Council of Biomechanics. She is a Fellow of the American Society of Mechanical Engineers and of the American Institute for Mechanical and Biological Engineering. She received the National Science Foundation Career Award in 2010, was selected as Midwest Mechanics Seminar Speaker in 2014, and received the Humboldt Research Award in 2016 and the ASME Ted Belytschko Applied Mechanics Award in 2021. Ellen is an All American Triathlete, a multiple Boston, Chicago, and New York marathon runner, and a Kona Ironman World Championship finisher.