Ceyron/conv-autodiff-table-frameworks Ceyron/conv-autodiff-table-frameworks

  • Stars
    star
    1
  • Language
    HTML
  • License
    MIT License
  • Created over 1 year ago
  • Updated 4 months ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
Ceyron/conv-autodiff-table-frameworks
github

Ceyron

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

A collection of pullback rules, using function calls from various deep learning libraries. This also explains the handling of batch and channel axes.

More Repositories

1

machine-learning-and-simulation

All the handwritten notes πŸ“ and source code files πŸ–₯️ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)
Jupyter Notebook
810
star
2

scientific-python-course

Slides + Source Code + Data for an introductory course to NumPy, Matplotlib, SciPy, Scikit-Learn & TensorFlow Keras
Jupyter Notebook
20
star
3

Tsunamis.jl

🌊 🌊 🌊 Parallel Shallow Water Equations Solver by Finite Volume Method and HLLE Riemann Solver in Julia.
Julia
17
star
4

StableFluids.jl

2D Stable Fluids & 3D Stable Fluids using the Fast Fourier Transformation implemented efficiently in Julia.
Julia
12
star
5

lid-driven-cavity-python

Solving the Navier-Stokes Equations in Python 🐍 simply using NumPy.
Python
11
star
6

pdequinox

Neural Emulator Architectures in JAX.
Python
8
star
7

expmath

Online visualization tool for basic engineering math concepts using flask and bokeh. Available online at http://expmath.math.nat.tu-bs.de/ (in German)
HTML
7
star
8

4k-turbulence-wallpapers

A collection of wallpapers
7
star
9

apebench

[Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (>46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled Training; Rollout Metrics)
Python
7
star
10

pinns-in-jax

Simple implementation of Physics-Informed Neural Networks for the solution of Partial Differential Equations in JAX (using Equinox and Optax)
Jupyter Notebook
6
star
11

exponax

Efficient Differentiable n-d PDE solvers in JAX.
Jupyter Notebook
6
star
12

taylor-green-vortex-julia

A simple pseudo-spectral solver for the Direct Numerical Simulation (DNS) of the 3D Taylor-Green Vortex in the Julia programming language
Julia
6
star
13

Lattice-Boltzmann-Method-JAX

Simple D2Q9 Lattice-Boltzmann-Method solver implemented in Python with JAX. Simulates the fluid motion of the van-Karman vortex street behind a cylinder.
Python
6
star
14

numerical_programming_cheatsheet

TeX
4
star
15

DeepONet-in-JAX

Simple implementation of Deep Operator Networks (DeepONets) in the JAX deep learning framework together with Equinox.
Jupyter Notebook
4
star
16

UNet-in-JAX

Simple 1d UNet in JAX & Equinox to solve the Poisson equation.
Jupyter Notebook
4
star
17

FNO-in-JAX

Simple implementation of Fourier Neural Operators (FNOs) in the JAX deep learning framework together with Equinox.
Jupyter Notebook
4
star
18

autodiff-table

An overview of major automatic differentiation primitive rules
HTML
2
star
19

pinns-in-julia

Simple implementation of Physics-Informed Neural Networks for the solution of Partial Differential Equations in Julia
Jupyter Notebook
2
star
20

expmath_2

New Version of Expmath, partiall using the old Expmath but inside new streamlit environment
Python
1
star