Update docs

Author: foldfelis

README.md

@@ -57,7 +57,6 @@ model = Chain(
     # project back to the scalar field of interest space
     Dense(64, 128, gelu),
     Dense(128, 1),
-    flatten
 )
 ```
 

docs/src/index.md

@@ -9,7 +9,7 @@ CurrentModule = NeuralOperators
 | **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`](../../example/FlowOverCircle).
+Example can be found in [`example/FlowOverCircle`](https://github.com/SciML/NeuralOperators.jl/tree/master/example/FlowOverCircle).
 
 ## Quick start
 
@@ -36,7 +36,6 @@ model = Chain(
     # project back to the scalar field of interest space
     Dense(64, 128, gelu),
     Dense(128, 1),
-    flatten
 )
 ```
 
@@ -53,7 +52,7 @@ model = FourierNeuralOperator(
 And then train as a Flux model.
 
 ```julia
-loss(𝐱, 𝐲) = Flux.Losses.mse(model(𝐱), 𝐲)
+loss(𝐱, 𝐲) = l₂loss(model(𝐱), 𝐲)
 opt = Flux.Optimiser(WeightDecay(1f-4), Flux.ADAM(1f-3))
 Flux.@epochs 50 Flux.train!(loss, params(model), data, opt)
 ```
@@ -85,4 +84,4 @@ opt = ADAM(learning_rate)
 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](../../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/master/example/Burgers/src/Burgers_deeponet.jl).

docs/src/introduction.md

@@ -8,23 +8,19 @@ an operator mapping from infinite-dimensional space ``(u, t)`` to infinite-dimen
 Neural operator learns a continuous function between two continuous function spaces.
 The kernel can be trained on different geometry, including regular Euclidean space or a graph topology.
 
-## Fourier Neural Operators
+## [Fourier Neural Operators](https://github.com/SciML/NeuralOperators.jl/blob/master/src/model.jl)
 
 Fourier neural operator (FNO) 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 operator.
 
-## Markov Neural Operators
+## [Markov Neural Operators](https://github.com/SciML/NeuralOperators.jl/blob/master/src/model.jl)
 
 Markov neural operator (MNO) learns a neural operator with Fourier operators.
 With only one time step information of learning, it can predict the following few steps with low loss
 by linking the operators into a Markov chain.
 
-## Deep Operator Network
+## [Deep Operator Network](https://github.com/SciML/NeuralOperators.jl/blob/master/src/DeepONet.jl)
 
 Deep operator network (DeepONet) learns a neural operator with the help of two sub-neural network structures described as the branch and the trunk network.
 The branch network is fed the initial conditions data, whereas the trunk is fed with the locations where the target(output) is evaluated from the corresponding initial conditions.
 It is important that the output size of the branch and trunk subnets is same so that a dot product can be performed between them.
-
-Currently, the `OperatorKernel` layer is provided in this work.
-As for model, there are `FourierNeuralOperator` and `MarkovNeuralOperator` provided.
-Please take a glance at [them](apis.html#Models).