kristpapadopoulos / nonlinear_dynamics

Predicting nonlinear dynamics with machine learning

Nonlinear Dynamics

Predicting nonlinear dynamics with machine learning techniques

1. TIme series mutual information delay embedding for phase portraits example Lorenz attractor - Time_Series-Delay_Embedding.ipynb

2. Future work

• S. Ouala et al., “Learning Latent Dynamics for Partially-Observed Chaotic Systems,” arXiv:1907.02452 [cs, stat], Jul. 2019.
• J. Pathak, Z. Lu, B. R. Hunt, M. Girvan, and E. Ott, “Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 27, no. 12, p. 121102, Dec. 2017.
• N. Dhir, A. R. Kosiorek, and I. Posner, “Bayesian Delay Embeddings for Dynamical Systems,” p. 12, 2017.