Autoregressive ResNet for Kuramoto-Sivashinsky (KS) in JAX скачать в хорошем качестве

Autoregressive ResNet for Kuramoto-Sivashinsky (KS) in JAX

Let's train a convolutional Residual Network (ResNet) to become an autoregressive emulator (neural operator) for the Kuramoto-Sivashinsky equation using the JAX deep learning library. Here is the code: https://github.com/Ceyron/machine-lea... ----- 👉 This educational series is supported by the world-leaders in integrating machine learning and artificial intelligence with simulation and scientific computing, Pasteur Labs and Institute for Simulation Intelligence. Check out https://simulation.science/ for more on their pursuit of 'Nobel-Turing' technologies (https://arxiv.org/abs/2112.03235 ), and for partnership or career opportunities. ------- 📝 : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): https://github.com/Ceyron/machine-lea... 📢 : Follow me on LinkedIn or Twitter for updates on the channel and other cool Machine Learning & Simulation stuff:   / felix-koehler   and   / felix_m_koehler   💸 : If you want to support my work on the channel, you can become a Patreon here:   / mlsim   🪙: Or you can make a one-time donation via PayPal: https://www.paypal.com/paypalme/Felix... ------- Timestamps: 00:00:00 Intro 00:01:59 About the KS equation 00:03:30 Imports 00:03:54 Defining Constants 00:05:58 Reference Simulator (ETDRK2) 00:07:32 Drawing Initial Conditions (ICs) 00:11:02 Autoregressive Rollout of Reference Trajectories 00:14:38 Visualizing a Trajectory 00:16:44 Pre-Processing the Train Trajectories 00:17:56 Redo Data Generation for Test Trajectories 00:19:54 Slicing two-snapshot windows out of Train Trajectories 00:28:45 Implementing a Res-Block 00:25:30 Implementing ResNet Architecture 00:40:31 Training Loop 00:50:59 Training Loss History 00:52:23 Error Rollout against test trajectories 00:59:35 Correlation Rollout against test trajectories 01:05:15 Sample prediction trajectory 01:07:24 Comparing the spectrum after decorrelation 01:18:00 Outro