Unrolled vs. Implicit Autodiff

Unrolled Autodiff of iterative Algorithms

UNet Tutorial in JAX

DeepONet Tutorial in JAX

Spectral Derivative in 3d using NumPy and the RFFT

NumPy.fft.rfft2 - real-valued spectral derivatives in 2D

2D Spectral Derivatives with NumPy.FFT

Softmax - Pullback/vJp rule

Softmax - Pushforward/Jvp rule

Fourier Neural Operators (FNO) in JAX

Custom Rollout transformation in JAX (using scan)

JAX.lax.scan tutorial (for autoregressive rollout)

Upgrade the KS solver in JAX to 2nd order

Simple KS solver in JAX

np.fft.rfft for spectral derivatives in Python

Spectral Derivative with FFT in NumPy

Physics-Informed Neural Networks in Julia

Physics-Informed Neural Networks in JAX (with Equinox & Optax)

Neural Networks using Lux.jl and Zygote.jl Autodiff in Julia

Neural Networks in Equinox (JAX DL framework) with Optax

Neural Networks in pure JAX (with automatic differentiation)

Neural Network learns sine function in NumPy/Python with backprop from scratch

Simple reverse-mode Autodiff in Python

Animating the learning process of a Neural Network

Neural Network learns Sine Function with custom backpropagation in Julia

Simple reverse-mode Autodiff in Julia - Computational Chain

Neural ODE - Pullback/vJp/adjoint rule

Neural ODEs - Pushforward/Jvp rule

L2 Loss (Least Squares) - Pullback/vJp rule

L2 Loss (Least Squares) - Pushforward/Jvp rule