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foldfelis committed Jul 7, 2022
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# NeuralOperators

| ![](https://github.com/foldfelis/NeuralOperators.jl/blob/master/example/FlowOverCircle/gallery/ans.gif?raw=true) | ![](https://github.com/foldfelis/NeuralOperators.jl/blob/master/example/FlowOverCircle/gallery/inferenced.gif?raw=true) |
| ![](https://github.com/foldfelis/NeuralOperators.jl/blob/main/example/FlowOverCircle/gallery/ans.gif?raw=true) | ![](https://github.com/foldfelis/NeuralOperators.jl/blob/main/example/FlowOverCircle/gallery/inferenced.gif?raw=true) |
|:----------------:|:--------------:|
| **Ground Truth** | **Inferenced** |

The demonstration shown above is Navier-Stokes equation learned by the `MarkovNeuralOperator` with only one time step information.
Example can be found in [`example/FlowOverCircle`](https://github.com/SciML/NeuralOperators.jl/tree/master/example/FlowOverCircle).
Example can be found in [`example/FlowOverCircle`](https://github.com/SciML/NeuralOperators.jl/tree/main/example/FlowOverCircle).

## Quick start

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parameters = params(model)
Flux.@epochs 400 Flux.train!(loss, parameters, [(xtrain, ytrain, grid)], opt, cb=evalcb)
```
A more complete example using DeepONet architecture to solve Burgers' equation can be found in the [examples](https://github.com/SciML/NeuralOperators.jl/blob/master/example/Burgers/src/Burgers_deeponet.jl).
A more complete example using DeepONet architecture to solve Burgers' equation can be found in the [examples](https://github.com/SciML/NeuralOperators.jl/blob/main/example/Burgers/src/Burgers_deeponet.jl).

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