Physics-informed machine learning for automatic model reduction in chemical reaction networks.

ABSTRACT: Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of Aβ fibril aggregation, a key factor in Alzheimer's disease. Central to the research is the introduction of an automatic reaction order model reduction framework, designed to optimize reduced-order kinetic models. This framework represents a shift in model construction, automatically determining the appropriate level of detail for reaction network modeling. The proposed approach signif

SUBMITTER: Pateras J 

PROVIDER: S-EPMC11889170 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature