refactor

Author: foldfelis

docs/src/apis.md

@@ -13,7 +13,9 @@ F'(s) = g(F(s)) \\
 v'(x) = \mathcal{F}^{-1} \{ F'(s) \}
 ```
 
-where ``v(x)`` and ``v'(x)`` denotes input and output function, ``\mathcal{F} \{ \cdot \}``, ``\mathcal{F}^{-1} \{ \cdot \}`` are Fourier transform, inverse Fourier transform, respectively. Function ``g`` is a linear transform for lowering Fouier modes.
+where ``v(x)`` and ``v'(x)`` denotes input and output function,
+``\mathcal{F} \{ \cdot \}``, ``\mathcal{F}^{-1} \{ \cdot \}`` are Fourier transform, inverse Fourier transform, respectively.
+Function ``g`` is a linear transform for lowering Fouier modes.
 
 ```@docs
 SpectralConv
@@ -29,7 +31,8 @@ Reference: [Fourier Neural Operator for Parametric Partial Differential Equation
 v_{t+1}(x) = \sigma(W v_t(x) + \mathcal{K} \{ v_t(x) \} )
 ```
 
-where ``v_t(x)`` is the input function for ``t``-th layer and ``\mathcal{K} \{ \cdot \}`` denotes spectral convolutional layer. Activation function ``\sigma`` can be arbitrary non-linear function.
+where ``v_t(x)`` is the input function for ``t``-th layer and ``\mathcal{K} \{ \cdot \}`` denotes spectral convolutional layer.
+Activation function ``\sigma`` can be arbitrary non-linear function.
 
 ```@docs
 FourierOperator