PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator

1 code implementation • 22 Jun 2023 • Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton

PyKoopman is a Python package for the data-driven approximation of the Koopman operator associated with a dynamical system.

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

PySensors: A Python Package for Sparse Sensor Placement

4 code implementations • 20 Feb 2021 • Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz

PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks.

Classification General Classification

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

Discovery of Physics from Data: Universal Laws and Discrepancies

1 code implementation • 19 Jun 2019 • Brian M. de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz

Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone.