88 citations · 168 across the 7 of their papers we have counts for
7 papers
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
Shaowu Pan, Eurika Kaiser, Brian M. de Silva +2
PyKoopman is a Python package for the data-driven approximation of the Koopman operator associated with a dynamical system. The Koopman operator is a principled linear embedding of…
Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel +8
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastab…
PySensors: A Python Package for Sparse Sensor Placement
Brian M. de Silva, Krithika Manohar, Emily Clark +3
PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks. Specifically, PySensors implements algorithms for data-…
Physics-informed machine learning for sensor fault detection with flight test data
Brian M. de Silva, Jared Callaham, Jonathan Jonker +7
We develop data-driven algorithms to fully automate sensor fault detection in systems governed by underlying physics. The proposed machine learning method uses a time series of typ…
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…
Discovery of Physics from Data: Universal Laws and Discrepancies
Brian M. de Silva, David M. Higdon, Steven L. Brunton +1
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 a…