| PySINDy: A comprehensive Python package for robust sparse system identification AA Kaptanoglu, BM de Silva, U Fasel, K Kaheman, AJ Goldschmidt, ... arXiv preprint arXiv:2111.08481, 2021 | 189 | 2021 |
| Robust flow reconstruction from limited measurements via sparse representation JL Callaham, K Maeda, SL Brunton Physical Review Fluids 4 (10), 103907, 2019 | 158 | 2019 |
| Promoting global stability in data-driven models of quadratic nonlinear dynamics AA Kaptanoglu, JL Callaham, A Aravkin, CJ Hansen, SL Brunton Physical Review Fluids 6 (9), 094401, 2021 | 101 | 2021 |
| Nonlinear stochastic modeling with Langevin regression JL Callaham, JC Loiseau, G Rigas, SL Brunton Proceedings of the Royal Society A 477 (2250), 2021 | 96 | 2021 |
| Learning dominant physical processes with data-driven balance models JL Callaham, JV Koch, BW Brunton, JN Kutz, SL Brunton Nature communications 12 (1), 1016, 2021 | 94 | 2021 |
| On the role of nonlinear correlations in reduced-order modelling JL Callaham, SL Brunton, JC Loiseau Journal of Fluid Mechanics 938, A1, 2022 | 56 | 2022 |
| Dimensionally consistent learning with buckingham pi J Bakarji, J Callaham, SL Brunton, JN Kutz Nature Computational Science 2 (12), 834-844, 2022 | 46 | 2022 |
| An empirical mean-field model of symmetry-breaking in a turbulent wake JL Callaham, G Rigas, JC Loiseau, SL Brunton Science Advances 8 (19), eabm4786, 2022 | 43 | 2022 |
| Physics-informed machine learning for sensor fault detection with flight test data BM de Silva, J Callaham, J Jonker, N Goebel, J Klemisch, D McDonald, ... arXiv preprint arXiv:2006.13380, 2020 | 40 | 2020 |
| Population annealing simulations of a binary hard-sphere mixture J Callaham, J Machta Physical Review E 95 (6), 063315, 2017 | 20 | 2017 |
| Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data BM de Silva, J Callaham, J Jonker, N Goebel, J Klemisch, D McDonald, ... AIAA Journal 59 (9), 3490-3503, 2021 | 11 | 2021 |
| Robust reconstruction of flow fields from limited measurements J Callaham, K Maeda, S Brunton Bulletin of the American Physical Society 63, 2018 | 5 | 2018 |
| Machine Learning to Discover Interpretable Models in Fluids and Plasmas A Kaptanoglu, J Callaham, C Hansen, S Brunton APS March Meeting Abstracts 2022, S49. 002, 2022 | 4 | 2022 |
| Data-driven stochastic modeling of coarse-grained dynamics with finite-size effects using Langevin regression J Snyder, JL Callaham, SL Brunton, JN Kutz Physica D: Nonlinear Phenomena 427, 133004, 2021 | 4 | 2021 |
| Multiscale model reduction for incompressible flows JL Callaham, JC Loiseau, SL Brunton Journal of Fluid Mechanics 973, A3, 2023 | 2 | 2023 |
| HydroGym: A Reinforcement Learning Control Framework for Fluid Dynamics L Paehler, J Callaham, S Ahnert, N Adams, S Brunton Bulletin of the American Physical Society, 2023 | 1 | 2023 |
| Multiscale model reduction for unsteady fluid flow J Callaham University of Washington, 2022 | 1 | 2022 |
| An open-source fluid-structure interaction code for anyone and everyone N OBrien, A Machado Burgos, S Balasubramanian, J Callaham, A Goza Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Network-based feedback control of Fluid Flows K Taira, S Brunton, C Shih, A Nair, CA Yeh, Z Bai, J Callaham | | 2022 |
| Unsupervised Learning of Dimensionless Groups and Minimally Parametrized Equations J Bakarji, S Brunton, N Kutz, J Callaham APS Division of Fluid Dynamics Meeting Abstracts, A31. 003, 2021 | | 2021 |
Steven L. BruntonProfessor, University of WashingtonEmail verificata su uw.edu