Code for "SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning" by Zolman et al.
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
a collection of modern sparse (regularized) linear regression algorithms.
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
Physically-informed model discovery of systems with nonlinear, rational terms using the SINDy-PI method. Contains functionality for spectral filtering/differentiation.
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal Society A.
MEDIDA: Model Error Discovery with Interpretability and Data Assimilation
Lorenz 63 Attractor, Kortweg - De Vries and Burgers equations, and wave stuff.
Using Filecoin Lilium Data for learning SINDy
Выпускная квалификационная работа бакалавра
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