Learning stochastic reaction-diffusion models from limited data using spatiotemporal features.

ABSTRACT: Pattern-forming stochastic systems arise throughout biology, with dynamic molecular waves observed in biochemical networks regulating critical cellular processes. Modeling these reaction-diffusion systems using handcrafted stochastic partial differential equations (PDEs) requires extensive trial-and-error tuning. Data-driven approaches for improved modeling are needed but have been hindered by data scarcity and noise. Here, we present a solution to the inverse problem of learning stochastic reaction-diffusion models from limited data by optimizing two spatiotemporal features: (1) stochastic dynamics and (2) spatiotemporal patterns. Combined with sparsity enforcement, this method identifies novel activator-inhibitor models with interpretable structure. We demonstrate robust learning from si

SUBMITTER: Abubaker-Sharif B 

PROVIDER: S-EPMC11952355 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature