Fix broken link for notebooks

yuehhua · Mar 6, 2022 · 351b70c · 351b70c
1 parent 91e49d0
commit 351b70c
Show file tree

Hide file tree

Showing 3 changed files with 3 additions and 3 deletions.
diff --git a/example/DoublePendulum/README.md b/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://foldfelis.github.io/NeuralOperators.jl/dev/assets/notebook/double_pendulum.jl.html).
+The result of this example can be found [here](https://neuraloperators.sciml.ai/dev/assets/notebook/double_pendulum.jl.html).
 
 ![](gallery/result.gif)
 

diff --git a/example/FlowOverCircle/README.md b/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://foldfelis.github.io/NeuralOperators.jl/dev/assets/notebook/mno.jl.html).
+The result of this example can be found [here](https://neuraloperators.sciml.ai/dev/assets/notebook/mno.jl.html).
 
 | **Ground Truth** | **Inferenced** |
 |:----------------:|:--------------:|

diff --git a/example/SuperResolution/README.md b/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://foldfelis.github.io/NeuralOperators.jl/dev/assets/notebook/super_resolution_mno.jl.html).
+The result of this example can be found [here](https://neuraloperators.sciml.ai/dev/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).