Data-Guided Physics-Informed Neural Networks for Solving Inverse
Problems in Partial Differential Equations
Data-Guided Physics-Informed Neural Networks for Solving Inverse
Problems in Partial Differential Equations
Physics-informed neural networks (PINNs) represent a significant advancement in scientific machine learning by integrating fundamental physical laws into their architecture through loss functions. PINNs have been successfully applied to solve various forward and inverse problems in partial differential equations (PDEs). However, a notable challenge can emerge during the early training …