Physics-informed machine learning

Physics-informed neural networks and time-series transformer for modeling of chemical reactors

Multiscale modeling of catalytical chemical reactors typically results in solving a system of partial differential equations (PDEs) or ordinary differential equations (ODEs). Despite significant progress, the numerical solution of such PDE or ODE …

Physical pooling functions in graph neural networks for molecular property prediction

Physical pooling functions