Search Results for author: Kathleen Champion

Found 4 papers, 3 papers with code

Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders

no code implementations13 Jan 2022 Joseph Bakarji, Kathleen Champion, J. Nathan Kutz, Steven L. Brunton

Here, we design a custom deep autoencoder network to learn a coordinate transformation from the delay embedded space into a new space where it is possible to represent the dynamics in a sparse, closed form.

Model Discovery

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

2 code implementations17 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

A unified sparse optimization framework to learn parsimonious physics-informed models from data

4 code implementations25 Jun 2019 Kathleen Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz

This flexible approach can be tailored to the unique challenges associated with a wide range of applications and data sets, providing a powerful ML-based framework for learning governing models for physical systems from data.

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