Supplementary data for Identifying recurrent flows in high-dimensional dissipative chaos from low-dimensional embeddings
Abstract
This dataset contains neural network weights, flow data, pod modes, and example guesses and upos for Kuramoto-Sivashinsky and Kolmogorov flow for the manuscript:Identifying recurrent flows in high-dimensional dissipative chaos from low-dimensional embeddingshttps://doi.org/10.48550/arXiv.2601.01590 Relevant Github repository:https://github.com/Pibe97/autodiff_loops
Fields of Study & Subjects
Model Reduction and Neural Networks
Quantum chaos and dynamical systems
Neural Networks and Reservoir Computing
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 20 August 2026
- Document type
- Preprint