On the data-driven identification of some dynamical systems, linear regression, NODEs ...
Associated papers :
Chen, R. T., Rubanova, Y., Bettencourt, J., & Duvenaud, D. (2018). Neural ordinary differential equations. arXiv preprint arXiv:1806.07366.
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the national academy of sciences, 113(15), 3932-3937.
Starter code for data generation :
https://github.com/ptandeo/AnDA
Next on the list :
- Partially observed systems
- Irregular observations
- Data-driven Koopman
Dependencies :
- numpy 1.19.2
- pytorch 1.10.0
- tqdm 4.51.0
- scipy 1.5.3
- matplotlib 3.2.1
- torchdiffeq 0.1.1