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machine-learning-and-simulation
All the handwritten notes ๐ and source code files ๐ฅ๏ธ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)Tsunamis.jl
๐ ๐ ๐ 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 ๐ 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.expmath_2
New Version of Expmath, partiall using the old Expmath but inside new streamlit environmentLove Open Source and this site? Check out how you can help us