neuraloperators jl

We have hosted the application neuraloperators jl in order to run this application in our online workstations with Wine or directly.

Quick description about neuraloperators jl:

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

Programming Language: Julia.
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