Project advances machine learning techniques for data driven approaches to complex dynamical systems from data. In particular, this project combines proper orthogonal decomposition (POD), with Heavy-Ball accelerated neural ODEs (HB-NODE) to significantly improve model efficiency and accuracy for complex dynamics. Furthermore, this project demonstrates robustness of HB-NODE to variabile initializations of the underlying dynamical systems.
We consider 4 primary types of experiments. A VAE model for predicting steady-state dynamics. A SEQ model for predicting steady-state dynamics from transient dynamics. And finally a PARAM model for training on several varrying parameterizations and predicting unseen parameterizations.
Our submitted work can be found on arXiv: https://arxiv.org/abs/2202.12373
Notes: after refactor and rebase, models parameters are currently unoptimized. A suggested parameter list can be found in ./bin/tuning.txt. Results will follow after rebase&refactor.
To ensure all necessary packages are installed consider running the following command. This will not update or adjust the version of any currently installed packages.
pip install -e .All datasets can be generated by runinng the following
sh ./bin/data_generator.sh python3All examples can be found in ./bin/*_examples.sh.
All examples can be run using
sh ./bin/experiments.sh python3``
