924 citations · 1.3k across the 6 of their papers we have counts for
6 papers
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders
Joseph Bakarji, Kathleen Champion, J. Nathan Kutz +1
A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens' theorem pro…
PySINDy: A comprehensive Python package for robust sparse system identification
Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel +9
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. PySIN…
PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
Brian M. de Silva, Kathleen Champion, Markus Quade +3
PySINDy is a Python package for the discovery of governing dynamical systems models from data. In particular, PySINDy provides tools for applying the sparse identification of nonli…
A unified sparse optimization framework to learn parsimonious physics-informed models from data
Kathleen Champion, Peng Zheng, Aleksandr Y. Aravkin +2
Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a uni…
Data-driven discovery of coordinates and governing equations
Kathleen Champion, Bethany Lusch, J. Nathan Kutz +1
The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse…
Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings
Kathleen Champion, Steven L. Brunton, J. Nathan Kutz
A major challenge in the study of dynamical systems is that of model discovery: turning data into models that are not just predictive, but provide insight into the nature of the un…