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machine-learning-and-simulation
All the handwritten notes π and source code files π₯οΈ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)scientific-python-course
Slides + Source Code + Data for an introductory course to NumPy, Matplotlib, SciPy, Scikit-Learn & TensorFlow KerasTsunamis.jl
π π π Parallel Shallow Water Equations Solver by Finite Volume Method and HLLE Riemann Solver in Julia.StableFluids.jl
2D Stable Fluids & 3D Stable Fluids using the Fast Fourier Transformation implemented efficiently in Julia.lid-driven-cavity-python
Solving the Navier-Stokes Equations in Python π simply using NumPy.pdequinox
Neural Emulator Architectures in JAX.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)4k-turbulence-wallpapers
A collection of wallpapersapebench
[Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (>46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled Training; Rollout Metrics)pinns-in-jax
Simple implementation of Physics-Informed Neural Networks for the solution of Partial Differential Equations in JAX (using Equinox and Optax)exponax
Efficient Differentiable n-d PDE solvers in JAX.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 languageLattice-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.numerical_programming_cheatsheet
DeepONet-in-JAX
Simple implementation of Deep Operator Networks (DeepONets) in the JAX deep learning framework together with Equinox.UNet-in-JAX
Simple 1d UNet in JAX & Equinox to solve the Poisson equation.FNO-in-JAX
Simple implementation of Fourier Neural Operators (FNOs) in the JAX deep learning framework together with Equinox.autodiff-table
An overview of major automatic differentiation primitive rulespinns-in-julia
Simple implementation of Physics-Informed Neural Networks for the solution of Partial Differential Equations in Juliaconv-autodiff-table-frameworks
A collection of pullback rules, using function calls from various deep learning libraries. This also explains the handling of batch and channel axes.Love Open Source and this site? Check out how you can help us