Learning chemical reactions from simulated and measured data

The aim of this project is to develop new machine learning approaches to learn parameters in ordinary differential equations (ODEs) from simulated and measured data related to chemical reactions. This project will develop new mathematical methods that can recover sparse ODE formulations, incorporating important constraints which are naturally satisfied by chemical reaction equations. The project will build on recent progress in the sparse recovery of nonlinear dynamical systems and will significantly develop them further.