Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology

Abstract

Objectives: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.

Method: Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorithm) are combined to achieve an in-silico model with the ability of self-learning and replicating feedback loops occurring in human physiology. Computational particles, used in Discrete Multiphysics to model biological systems, are associated to (computational) neurons: Reinforcement Learning trains these neurons to behave like they would in real biological systems.

Results: As benchmark/validation, we use the case of peristalsis in the oesophagus. Results show that the in-silico model effectively learns by itself how to propel the bolus in the oesophagus.

Conclusions: The combination of first principles modelling (e.g. multiphysics) and machine learning (e.g. Reinforcement Learning) represents a new powerful tool for in-silico modelling of human physiology. Biological feedback loops occurring, for instance, in peristaltic or metachronal motion, which until now could not be accounted for in in-silico models, can be tackled by the proposed technique.

Keywords: Coupling first-principles models with machine learning; Discrete multiphysics; Particle-based computational methods; Reinforcement Learning.