Learning invariant representations of time-homogeneous stochastic dynamical systems - ICLR 2024

Warning! The repository is in the process of being updated to the latest version of kooplearn. Thanks for your patience!

Timeline:

Mar 23, 2024:

• The fluid flow data will be released as a GitHub Release which is already drafted. It will be published on the final commit of the code.
• The Fluid Flow example is now fully re-implemented. Testing that DPNets-relaxed reproduces the results of the old implementation.

Mar 20, 2024:

• Working on the fluid flow. Need to fix evaluate_model.

Mar 19, 2024:

• Working on the fluid flow example. Refactoring to the new kooplearn API. Adding Kernel Baselines

Roadmap:

• General

  • Create requirements.txt
  • Write instructions to run experiments.

• Logistic Map

  • Check that everything works with kooplearn==1.1.0

• Langevin Dynamics. See also this Github Gist.

  • Port the code in torch
  • Re-run simulations

• Ordered MNIST

  • Almost done. Use what already in kooplearn.

• Chignolin

  • Re-run the Nystroem baseline with kooplearn.
  • Check De Shaw policy about data, and understand whether the checkpoints can be shared or not.
  • (Ideally) embed schnetpack's implementation into kooplearn.