Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations¶
Why this mattered¶
TBD
Abstract¶
(no abstract available)
Related¶
- cite → Multilayer feedforward networks are universal approximators — Physics-informed neural networks rely on the universal approximation capacity of feedforward networks to represent PDE solution functions.
- cite → ImageNet classification with deep convolutional neural networks — Physics-informed neural networks cite AlexNet as evidence that deep neural networks can learn high-dimensional nonlinear representations effectively.
- enables ← Multilayer feedforward networks are universal approximators — The universal approximation theorem enabled PINNs by justifying neural networks as flexible function approximators for PDE solution fields.