Machine Learning & Simulation
Explaining topics of 🤖 Machine Learning & 🌊 Simulation with intuition, visualization and code.
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Hey,
welcome to my channel of explanatory videos for Machine Learning & Simulation. I cover topics from Probabilistic Machine Learning, High-Performance Computing, Continuum Mechanics, Numerical Analysis, Computational Fluid Dynamics, Automatic Differentiation and Adjoint Methods. Many videos include hands-on coding parts in Python, Julia, or C++. The videos also showcase the application of the topics in modern libraries like JAX, TensorFlow Probability, NumPy, SciPy, FEniCS, PETSc and many more.
All material is also available on the GitHub Repo of the channel: https://github.com/Ceyron/machine-learning-and-simulation
Enjoy :) And please leave feedback.
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If you want to make a one-time donation, you can do so via PayPal: https://paypal.me/FelixMKoehler
On Autoregressive Neural Emulators for PDEs | Talk @ IRMA Strasbourg
Transformer Neural Operator in JAX
Full Lyapunov Spectrum of Chaotic Lorenz System using JAX
Podcast with @JousefM | My Channel & Research, Autodiff, Physics & Deep Learning, Education...
APEBench Talk @ Pasteur Labs Journal Club
APEBench Quickstart
Largest Lyapunov Exponent using Autodiff in JAX/Python
Lyapunov Exponent in NumPy
Autoregressive Neural Emulator for Lorenz in JAX
Parallel Lorenz Simulation in JAX
Lorenz Map in NumPy
Lorenz Simulator in NumPy
Autoregressive ResNet for Kuramoto-Sivashinsky (KS) in JAX
Autodiff and Adjoints for Differentiable Physics
Reverse Mode Autodiff in Python (general compute graph)
Unrolled vs. Implicit Autodiff
Unrolled Autodiff of iterative Algorithms
UNet Tutorial in JAX
DeepONet Tutorial in JAX
Spectral Derivative in 3d using NumPy and the RFFT
NumPy.fft.rfft2 - real-valued spectral derivatives in 2D
2D Spectral Derivatives with NumPy.FFT
Softmax - Pullback/vJp rule
Softmax - Pushforward/Jvp rule
Fourier Neural Operators (FNO) in JAX
Custom Rollout transformation in JAX (using scan)
JAX.lax.scan tutorial (for autoregressive rollout)
Upgrade the KS solver in JAX to 2nd order
Simple KS solver in JAX
np.fft.rfft for spectral derivatives in Python