Hoffmann, Moritz https://orcid.org/0000-0002-9533-8586
Scherer, Martin https://orcid.org/0000-0002-7983-4387
Hempel, Tim https://orcid.org/0000-0002-0073-9353
Mardt, Andreas https://orcid.org/0000-0002-7353-6063
de Silva, Brian https://orcid.org/0000-0003-0944-900X
Husic, Brooke E https://orcid.org/0000-0002-8020-3750
Klus, Stefan https://orcid.org/0000-0002-9672-3806
Wu, Hao https://orcid.org/0000-0002-2170-0618
Kutz, Nathan https://orcid.org/0000-0002-6004-2275
Brunton, Steven L https://orcid.org/0000-0002-6565-5118
Noé, Frank https://orcid.org/0000-0003-4169-9324
Funding for this research was provided by:
National Natural Science Foundation of China (20JC1413500)
Bundesministerium für Bildung und Forschung (BIFOLD)
Shanghai Municipal Science and Technology Commission (20JC1413500)
Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)
Central University Basic Research Fund of China (22120210133)
Berlin Mathematics Research Center MATH+ (A1-6)
H2020 European Research Council (CoG 772230 "ScaleCell")
Division of Mathematical Sciences (1440415)
National Science Foundation (2112085)
Deutsche Forschungsgemeinschaft (SFB 1114 (Projects A04)
Article Title: Deeptime: a Python library for machine learning dynamical models from time series data
Journal Title: Machine Learning: Science and Technology
Article Type: paper
Copyright Information: © 2021 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2021-11-05
Date Accepted: 2021-11-26
Online publication date: 2021-12-10