Multiscale modeling meets machine learning: What can we learn?

TL;DR

This review explores how integrating machine learning with multiscale modeling can enhance biological system analysis, especially in handling sparse data and complex dynamics, fostering interdisciplinary collaboration.

Contribution

It identifies opportunities and challenges in combining machine learning with multiscale modeling for biomedical applications, highlighting potential benefits and open questions.

Findings

Machine learning can incorporate physics-based constraints to improve robustness.

Multiscale modeling can create surrogate models and analyze system dynamics.

The review discusses current state, applications, and future challenges.

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.