This is the Linux app named NeuralOperators.jl whose latest release can be downloaded as v0.6.0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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NeuralOperators.jl
DESCRIPTION
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
- Exemples disponibles
- Documents disponibles
- 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
Langage de programmation
Julia
Catégories
This is an application that can also be fetched from https://sourceforge.net/projects/neuraloperators-jl.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
