canonize docs

Author: ArnoStrouwen

README.md

@@ -1,8 +1,7 @@
 # NeuralOperators
 
 [![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](http://neuraloperators.sciml.ai/stable/)
-[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/dev/modules/NeuralOperators/)
+[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/NeuralOperators/stable/)
 
 [![codecov](https://codecov.io/gh/SciML/NeuralOperators.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/NeuralOperators.jl)
 [![Build Status](https://github.com/SciML/NeuralOperators.jl/workflows/CI/badge.svg)](https://github.com/SciML/NeuralOperators.jl/actions?query=workflow%3ACI)

docs/make.jl

@@ -16,7 +16,7 @@ makedocs(bib,
          sitename = "NeuralOperators.jl",
          format = Documenter.HTML(;
                                   prettyurls = get(ENV, "CI", "false") == "true",
-                                  canonical = "http://neuraloperators.sciml.ai",
+                                  canonical = "https://docs.sciml.ai/NeuralOperators/stable/",
                                   assets = String[]),
          pages = pages)
 

example/DoublePendulum/README.md

@@ -5,7 +5,7 @@ In is example, instead of learning from a well-described equation of motion,
 we train the model with the famous dataset provided by IBM.
 The equation of motion to the real experiments of double pendulum is learned by a two-dimensional Fourier neural operator.
 It learns to inference the next 30 steps with the given first 30 steps.
-The result of this example can be found [here](https://neuraloperators.sciml.ai/dev/assets/notebook/double_pendulum.jl.html).
+The result of this example can be found [here](https://docs.sciml.ai/NeuralOperators/stable/assets/notebook/double_pendulum.jl.html).
 
 ![](gallery/result.gif)
 

example/FlowOverCircle/README.md

@@ -1,7 +1,7 @@
 # Flow over the circle
 
 The time dependent Navier-Stokes equation is learned by the `MarkovNeuralOperator` with only one time step information.
-The result of this example can be found [here](https://neuraloperators.sciml.ai/dev/assets/notebook/mno.jl.html).
+The result of this example can be found [here](https://docs.sciml.ai/NeuralOperators/stable/assets/notebook/mno.jl.html).
 
 | **Ground Truth** | **Inferenced** |
 |:----------------:|:--------------:|

example/SuperResolution/README.md

@@ -1,7 +1,7 @@
 # Super Resolution
 
 The time dependent Navier-Stokes equation is learned by the `MarkovNeuralOperator` with only one time step information.
-The result of this example can be found [here](https://neuraloperators.sciml.ai/dev/assets/notebook/super_resolution_mno.jl.html).
+The result of this example can be found [here](https://docs.sciml.ai/NeuralOperators/stable/assets/notebook/super_resolution_mno.jl.html).
 
 Apart from just training a MNO, here, we train the model with lower resolution (96x64) and inference result with higher resolution (192x128).