Progress with Stochastic Gradient Descent part1(Machine Learning)
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks(arXiv) Author : Ye Li, Song-Can Chen, Sheng-Jun Huang Abstract : Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training failures when the target functions to be approximated…
Progress with Stochastic Gradient Descent part1(Machine Learning) Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks(arXiv) Author : Ye Li, Song-Can Chen, Sheng-Jun Huang Abstract : Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training failures when the target functions to be approximated… deep-learning
Progress Stochastic Gradient Descent part1Machine Learning Implicit Stochastic Gradient Descent Training Physicsinformed Neural NetworksarXiv Author Ye Li SongCan Chen ShengJun Huang Abstract Physicsinformed neural network PINNs effectively demonstrated solving forward inverse differential equation problem still trapped training failure target function approximated… deeplearning