Path sampling of recurrent neural networks by incorporating known physics

Abstract

Adding prior experimentally or theoretically obtained knowledge to the training of recurrent neural networks may be challenging due to their feedback nature with arbitrarily long memories. The authors propose a path sampling approach that allows to include generic thermodynamic or kinetic constraints for learning of time series relevant to molecular dynamics and quantum systems.