add docstring for models

Author: foldfelis

README.md

@@ -20,9 +20,13 @@ from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t).
 Neural operator learns a continuous function between two continuous function spaces.
 The kernel can be trained on different geometry, which is learned from a graph.
 
-Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation.
+**Fourier neural operator** 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 operator** 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.
+
 Currently, the `FourierOperator` layer is provided in this work.
 As for model, there are `FourierNeuralOperator` and `MarkovNeuralOperator` provided. Please take a glance at them [here](src/model.jl).
 
@@ -44,7 +48,7 @@ model = Chain(
 )
 ```
 
-Or you can just call:
+Or one can just call:
 
 ```julia
 model = FourierNeuralOperator(

docs/src/index.md

@@ -14,6 +14,8 @@ The demonstration showing above is Navier-Stokes equation learned by the `Markov
 Example can be found in [`example/FlowOverCircle`](https://github.com/foldfelis/NeuralOperators.jl/tree/master/example/FlowOverCircle).
 The result is also provided [here](assets/notebook/mno.jl.html)
 
+## Abstract
+
 Neural operator is a novel deep learning architecture.
 It learns a operator, which is a mapping between infinite-dimensional function spaces.
 It can be used to resolve [partial differential equations (PDE)](https://en.wikipedia.org/wiki/Partial_differential_equation).
@@ -22,12 +24,16 @@ from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t).
 Neural operator learns a continuous function between two continuous function spaces.
 The kernel can be trained on different geometry, which is learned from a graph.
 
-Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation.
+**Fourier neural operator** 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 operator** 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.
+
 Currently, the `FourierOperator` layer is provided in this work.
 As for model, there are `FourierNeuralOperator` and `MarkovNeuralOperator` provided.
-Please take a glance at them [here](https://github.com/foldfelis/NeuralOperators.jl/blob/master/src/model.jl).
+Please take a glance at them [here](apis.html#Models).
 
 ## Quick start
 
@@ -55,7 +61,7 @@ model = Chain(
 )
 ```
 
-Or you can just call:
+Or one can just call:
 
 ```julia
 model = FourierNeuralOperator(

src/model.jl

@@ -2,6 +2,16 @@ export
     FourierNeuralOperator,
     MarkovNeuralOperator
 
+"""
+    FourierNeuralOperator(;
+        ch=(2, 64, 64, 64, 64, 64, 128, 1),
+        modes=(16, ),
+        σ=gelu
+    )
+
+Fourier neural operator 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.
+"""
 function FourierNeuralOperator(;
     ch=(2, 64, 64, 64, 64, 64, 128, 1),
     modes=(16, ),
@@ -19,6 +29,17 @@ function FourierNeuralOperator(;
     )
 end
 
+"""
+    MarkovNeuralOperator(;
+        ch=(1, 64, 64, 64, 64, 64, 1),
+        modes=(24, 24),
+        σ=gelu
+    )
+
+Markov neural operator 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.
+"""
 function MarkovNeuralOperator(;
     ch=(1, 64, 64, 64, 64, 64, 1),
     modes=(24, 24),