Felix Kรถhler (@Ceyron)
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Ceyron

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1

machine-learning-and-simulation

All the handwritten notes ๐Ÿ“ and source code files ๐Ÿ–ฅ๏ธ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)
Jupyter Notebook
810
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2

scientific-python-course

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

Tsunamis.jl

๐ŸŒŠ ๐ŸŒŠ ๐ŸŒŠ Parallel Shallow Water Equations Solver by Finite Volume Method and HLLE Riemann Solver in Julia.
Julia
17
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4

StableFluids.jl

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

lid-driven-cavity-python

Solving the Navier-Stokes Equations in Python ๐Ÿ simply using NumPy.
Python
11
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6

pdequinox

Neural Emulator Architectures in JAX.
Python
8
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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
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8

4k-turbulence-wallpapers

A collection of wallpapers
7
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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
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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
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11

exponax

Efficient Differentiable n-d PDE solvers in JAX.
Jupyter Notebook
6
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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
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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
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14

numerical_programming_cheatsheet

TeX
4
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15

DeepONet-in-JAX

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

UNet-in-JAX

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

FNO-in-JAX

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

autodiff-table

An overview of major automatic differentiation primitive rules
HTML
2
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19

pinns-in-julia

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

conv-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.
HTML
1
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21

expmath_2

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