Data-driven model discovery and model selection for noisy biological systems.

ABSTRACT: Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and is limited in its ability to incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics o

SUBMITTER: Wu X 

PROVIDER: S-EPMC11753677 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature