Learning Chemical Reaction Networks

This lecture covers the sparse learning of chemical reaction networks from trajectory data, focusing on data-based methods for dynamical systems, forward and backward problems, and learning approaches using basis functions and log-likelihood functions. The instructor presents theoretical results and tools, along with numerical examples of predator-prey systems and intracellular viral infections.