DeepKoopman

neural networks to learn Koopman eigenfunctions

Code for the paper "Deep learning for universal linear embeddings of nonlinear dynamics" by Bethany Lusch, J. Nathan Kutz, and Steven L. Brunton

To run code:

1. Clone respository.
2. In the data directory, recreate desired dataset(s) by running DiscreteSpectrumExample, Pendulum, FluidFlowOnAttractor, and/or FluidFlowBox in Matlab. (or email to ask for the datasets)
3. Back in the main directory, run desired experiment(s) with python.

Notes on running the Python experiments:

• A GPU is recommended but not required. The code can be run on a GPU or CPU without any changes.
• The paper contains results on the four datasets. These were the best results from running scripts that do a random parameter search (DiscreteSpectrumExampleExperiment.py, PendulumExperiment.py, FluidFlowOnAttractorExperiment.py, and FluidFlowBoxExperiment.py).
• To train networks using the specific parameters that produced the results in the paper instead of doing a parameter search, run DiscreteSpectrumExampleExperimentBestParams.py, PendulumExperimentBestParams.py, FluidFlowOnAttractorExperimentBestParams.py, and FluidFlowBoxExperimentBestParams.py.
• The experiment scripts include a loop over 200 random experiments (random parameters and random initializations of weights). You'll probably want to kill off the script earlier than that!
• Each random experiment can run up to params['max_time'] (in these experiments, 4 or 6 hours) but may be automatically terminated earlier if the error is not decreasing enough. If one experiment is not doing well, the script moves on to another random experiment.
• If the code decides to end an experiment, it saves the current results. It also saves every hour.