Felix Köhler's repositories

machine-learning-and-simulation

All the handwritten notes 📝 and source code files 🖥️ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)

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scientific-python-course

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

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Tsunamis.jl

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

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lid-driven-cavity-python

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

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StableFluids.jl

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

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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)

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4k-turbulence-wallpapers

A collection of wallpapers

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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.

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pinns-in-jax

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

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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

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DeepONet-in-JAX

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

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UNet-in-JAX

Simple 1d UNet in JAX & Equinox to solve the Poisson equation.

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FNO-in-JAX

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

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autodiff-table

An overview of major automatic differentiation primitive rules

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pinns-in-julia

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

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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.

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burgers-timestepper-table

A collection of possible timesteppers for the Burgers equation.

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ceyron.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

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CUDAPreconditioners.jl

Convenience wrappers to incomplete factorizations from CUSPARSE to be used for iterative solvers of sparse linear systems on the GPU

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fourier-table

Relating functions to their discrete Fourier coefficients

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implicit-autodiff-table

A collection of advanced autodiff primitive that built upon the implicit function theorem both for discrete problems (like solving linear systems of equations) and (semi-)continuous problems (like differential equations)

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JAXFLUIDS

Differentiable Fluid Dynamics Package

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learning-setup-table

An overview of various training setups for learning Neural timesteppers that approximate the step-wise solution to Partial Differential Equations (PDEs)

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predictor-learning-setups

A collection of learning setups for predictors, their primal passes and all logical backward passes. Created with draw.io

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