Physics-driven neural networks-based simulation system (PhyNNeSS)

This is a technique to simulated the response of continua undergoing large nonlinear deformations using radial basis function neural networks which have been exhaustively trained on “response surfaces” in pre-computational steps performed on full finite element models. Computations are highly scalable as the number of neurons in the network may be dynamically chosen to control computational speed,  without the need  to remesh. Higher order polynomial reproducing neural networks have been developed to improve prediction accuracy.