The deployment of high-performance simula- tions coupling PDEs and machine learning to complex problems necessitates the composition of capabilities provided by machine learning and PDE-based frame- works. In this approach, the PDE is used as an inductive bias enabling the coupled model to rely on fundamental physical laws while requiring less training data. PDE-based machine learning tech- niques are designed to address this limitation. However, in many realistic applications PDE modelling provides an incom- plete description of the physics of interest. Physics-driven machine learning models coupling PyTorch and Firedrake(arXiv)Ībstract : Partial differential equations (PDEs) are central to describing and modelling com- plex physical systems that arise in many disciplines across science and engineer- ing.New develoments in Mathematical Software 2023 part2(Machine Learning Research)
0 kommentar(er)
