Integral Chemical Reaction Neural Networks

This paper presents Integral Chemical Reaction Neural Networks (iCRNNs), a physics-constrained neural network framework designed to discover the structure and kinetics of chemical reaction networks from time-series concentration data. The approach addresses the fundamental challenge in chemical kinetics where traditional methods rely on prior mechanistic assumptions or suffer from high computational cost and noise sensitivity.