tag:joss.theoj.org,2005:/papers/by/Brian%20de%20SilvaJournal of Open Source Software2024-02-25T16:09:51ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/45532024-02-25T16:09:51Z2024-02-26T00:00:51ZPyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operatoracceptedv1.0.32023-06-20 18:07:16 UTC942024-02-25 16:09:51 UTC920245881ShaowuPanDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195, United States, Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States0000-0002-2462-362XEurikaKaiserDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States0000-0001-6049-0812BrianM.de SilvaDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195, United States0000-0003-0944-900XJ.NathanKutzDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195, United States0000-0002-6004-2275StevenL.BruntonDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States0000-0002-6565-511810.21105/joss.05881https://doi.org/10.5281/zenodo.10685233Pythonhttps://joss.theoj.org/papers/10.21105/joss.05881.pdfdynamical systems, Koopman operator, system identification, machine learning, neural networkstag:joss.theoj.org,2005:Paper/31222022-01-29T01:16:01Z2022-01-30T00:01:12ZPySINDy: A comprehensive Python package for robust sparse system identificationacceptedv1.2.32021-10-21 18:46:09 UTC692022-01-29 01:16:01 UTC720223994AlanA.KaptanogluDepartment of Physics, University of WashingtonBrianM.de SilvaDepartment of Applied Mathematics, University of WashingtonUrbanFaselDepartment of Mechanical Engineering, University of WashingtonKadierdanKahemanDepartment of Mechanical Engineering, University of WashingtonAndyJ.GoldschmidtDepartment of Physics, University of WashingtonJaredCallahamDepartment of Mechanical Engineering, University of WashingtonCharlesB.DelahuntDepartment of Applied Mathematics, University of WashingtonZacharyG.NicolaouDepartment of Applied Mathematics, University of WashingtonKathleenChampionDepartment of Applied Mathematics, University of WashingtonJean-ChristopheLoiseauArts et Métiers Institute of Technology, CNAM, DynFluid, HESAM UniversitéJ.NathanKutzDepartment of Applied Mathematics, University of WashingtonStevenL.BruntonDepartment of Mechanical Engineering, University of Washington10.21105/joss.03994https://doi.org/10.5281/zenodo.5842612Pythonhttps://joss.theoj.org/papers/10.21105/joss.03994.pdfdynamical systems, sparse regression, model discovery, system identification, machine learningtag:joss.theoj.org,2005:Paper/21282021-02-21T10:56:26Z2021-02-27T08:11:31ZPySensors: A Python package for sparse sensor placementacceptedv.0.3.02020-10-24 18:42:44 UTC582021-02-21 10:56:26 UTC620212828BrianM.de SilvaDepartment of Applied Mathematics, University of Washington0000-0003-0944-900XKrithikaManoharDepartment of Mechanical Engineering, University of Washington0000-0002-1582-6767EmilyClarkDepartment of Physics, University of WashingtonBingniW.BruntonDepartment of Biology, University of Washington0000-0002-4831-3466J.NathanKutzDepartment of Applied Mathematics, University of Washington0000-0002-6004-2275StevenL.BruntonDepartment of Mechanical Engineering, University of Washington0000-0002-6565-511810.21105/joss.02828https://doi.org/10.5281/zenodo.4542530Pythonhttps://joss.theoj.org/papers/10.21105/joss.02828.pdfmachine learningtag:joss.theoj.org,2005:Paper/15102020-05-18T20:15:54Z2021-02-15T11:31:00ZPySINDy: A Python package for the sparse identification of nonlinear dynamical systems from dataacceptedv0.12.02020-02-11 01:09:30 UTC492020-05-18 20:15:54 UTC520202104BrianM.de SilvaDepartment of Applied Mathematics, University of WashingtonKathleenChampionDepartment of Applied Mathematics, University of WashingtonMarkusQuadeAmbrosys GmbHJean-ChristopheLoiseauÉcole Nationale Supérieure des Arts et MétiersJ.NathanKutzDepartment of Applied Mathematics, University of WashingtonStevenL.BruntonDepartment of Mechanical Engineering, University of Washington, Department of Applied Mathematics, University of Washington10.21105/joss.02104https://doi.org/10.5281/zenodo.3832319Pythonhttps://joss.theoj.org/papers/10.21105/joss.02104.pdfdynamical systems, sparse regression, model discovery, system identification, machine learning