PySINDy: A comprehensive Python package for robust sparse system identification

1 code implementation • 12 Nov 2021 • Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Andy J. Goldschmidt, Jared L. Callaham, Charles B. Delahunt, Zachary G. Nicolaou, Kathleen Champion, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton

Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community.

Model Discovery regression

Deep Recurrent Encoder: A scalable end-to-end network to model brain signals

1 code implementation • 3 Mar 2021 • Omar Chehab, Alexandre Defossez, Jean-Christophe Loiseau, Alexandre Gramfort, Jean-Remi King

Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics.

PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data

2 code implementations • 17 Apr 2020 • Brian M. de Silva, Kathleen Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton

PySINDy is a Python package for the discovery of governing dynamical systems models from data.

Dynamical Systems Computational Physics