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reproducible docs
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docs/make.jl

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@@ -2,6 +2,9 @@ using NeuralOperators
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using Documenter
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using DocumenterCitations
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cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
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cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
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bib = CitationBibliography(joinpath(@__DIR__, "bibliography.bib"), sorting = :nyt)
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DocMeta.setdocmeta!(NeuralOperators, :DocTestSetup, :(using NeuralOperators);

docs/src/index.md

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Flux.@epochs 400 Flux.train!(loss, parameters, [(xtrain, ytrain, grid)], opt, cb=evalcb)
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```
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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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## Reproducibility
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```@raw html
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<details><summary>The documentation of this SciML package was built using these direct dependencies,</summary>
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```
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```@example
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using Pkg # hide
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Pkg.status() # hide
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```
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```@raw html
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</details>
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```
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```@raw html
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<details><summary>and using this machine and Julia version.</summary>
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```
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```@example
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using InteractiveUtils # hide
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versioninfo() # hide
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```
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```@raw html
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</details>
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```
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```@raw html
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<details><summary>A more complete overview of all dependencies and their versions is also provided.</summary>
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```
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```@example
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using Pkg # hide
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Pkg.status(;mode = PKGMODE_MANIFEST) # hide
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```
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```@raw html
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</details>
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```
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```@raw html
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You can also download the
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<a href="
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```
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```@eval
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using TOML
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version = TOML.parse(read("../../Project.toml",String))["version"]
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name = TOML.parse(read("../../Project.toml",String))["name"]
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link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Manifest.toml"
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```
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```@raw html
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">manifest</a> file and the
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<a href="
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```
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```@eval
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using TOML
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version = TOML.parse(read("../../Project.toml",String))["version"]
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name = TOML.parse(read("../../Project.toml",String))["name"]
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link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Project.toml"
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```
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```@raw html
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">project</a> file.
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```

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