Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function spaces. The kernel can be trained on different geometry, which is learned from a graph. Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation. It performs Fourier transformation across infinite-dimensional function spaces and learns better than neural operators. Markov neural operator learns a neural operator with Fourier operators.
Features
- Fourier Neural Operator
- Examples available
- Documentation available
- Markov neural operator learns a neural operator with Fourier operators
- DeepONet operator (Deep Operator Network) learns a neural operator
- You can again specify loss, optimization and training parameters just as you would for a simple neural network with Flux
Project Samples
