Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data

My Master Thesis in the area of Data-Driven Control Engineering

Short intro to scientific machine learning using physics informed neuronal networks. I used PyTorch as a framework.

MATLAB files for discovery of Poincaré maps

Code for discovering slow timescale dynamics using the SINDy method

EEG Seizure Detection System using SINDy and Machine Learning

Code associated to the paper "Data-driven stabilization of periodic orbits"

Using data driven numerical methods to perform system identification, model reduction, and design controllers.

Sparse Identification of Non-Linear Dynamics (SINDy) is used to identify and discover non-linear dynamics of wake & lift oscillator Vortex Induced Vibration (VIV) systems. 2 Reduced order VIV systems, namely, wake oscillator proposed by Fachinetti [1] (2003) & an older model of lift oscillator proposed by Hartlen & Curie [2] (1970) are studied.

Jupyter notebooks para uso didático em conjunto com o artigo "Introduçao a redes neurais para fisico" da Revista Brasileira de Ensino de Fisica

This repository focuses on the reconstruction of the Lorenz system, a well-known chaotic dynamical system, using the Sparse Identification of Nonlinear Dynamical systems (SINDy) algorithm. It aims to identify the governing equations of chaotic systems from data.

Code for my BSc thesis about SINDy at Bocconi University

SINDY (Sparse Identification of Non Linear Dynamics) ERP SALES NON LINEAL DISCRETE STOCASTIC PROCESS