| Dynamic mode decomposition for compressive system identification Z Bai, E Kaiser, JL Proctor, JN Kutz, SL Brunton AIAA Journal 58 (2), 561-574, 2020 | 115 | 2020 |
| Low-dimensional approach for reconstruction of airfoil data via compressive sensing Z Bai, T Wimalajeewa, Z Berger, G Wang, M Glauser, PK Varshney AIAA journal 53 (4), 920-933, 2015 | 90 | 2015 |
| Data-driven methods in fluid dynamics: Sparse classification from experimental data Z Bai, SL Brunton, BW Brunton, JN Kutz, E Kaiser, A Spohn, BR Noack Whither turbulence and big data in the 21st century?, 323-342, 2017 | 58 | 2017 |
| Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators Z Bai, L Peng Adv. Model. and Simul. in Eng. Sci. 8 (1), 28, 2021 | 15 | 2021 |
| Randomized methods to characterize large-scale vortical flow networks Z Bai, NB Erichson, M Gopalakrishnan Meena, K Taira, SL Brunton PloS one 14 (11), e0225265, 2019 | 14 | 2019 |
| Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive GM Wallace, Z Bai, R Sadre, T Perciano, N Bertelli, S Shiraiwa, EW Bethel, ... Journal of Plasma Physics 88 (4), 895880401, 2022 | 12 | 2022 |
| Physics based compressive sensing approach applied to airfoil data collection and analysis Z Bai, T Wimalajeewa, ZP Berger, G Wang, M Glauser, PK Varshney 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and …, 2013 | 8 | 2013 |
| Physics based compressive sensing approach applied to airfoil data collection and analysis. AIAA Paper 2013-0772 Z Bai, T Wimalajeewa, Z Berger, G Wang, M Glauser, PK Varshney 51st Aerospace Sciences Meeting, 2013 | 8* | 2013 |
| Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation M Avaylon, R Sadre, Z Bai, T Perciano Advances in Artificial Intelligence and Machine Learning 2 (01), 288-302, 2022 | 3 | 2022 |
| Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches Á Sánchez-Villar, Z Bai, N Bertelli, EW Bethel, J Hillairet, T Perciano, ... Nuclear Fusion 64 (9), 096039, 2024 | 1 | 2024 |
| FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural Networks Z Bai, X Wei, W Tang, L Oliker, Z Lin, S Williams arXiv preprint arXiv:2404.17466, 2024 | 1 | 2024 |
| Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models GM Wallace, Z Bai, N Bertelli, EW Bethel, T Perciano, S Shiraiwa, ... AIP Conference Proceedings 2984 (1), 2023 | 1 | 2023 |
| Deep learning based aerosol microphysics surrogate model for E3SM Z Bai, H Wan, T Hassan, K Zhang, A Almgren AGU24, 2024 | | 2024 |
| Sparsified time-dependent Fourier neural operators for fusion simulations MM Rahman, Z Bai, JR King, CR Sovinec, X Wei, S Williams, Y Liu Physics of Plasmas 31 (12), 2024 | | 2024 |
| On the development of robust real-time capable ICRF modeling via machine learning Á Sánchez Villar, Z Bai, N Bertelli, EW Bethel, J Hillairet, T Perciano, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings (FTL) v1. 0 Z Bai, L Oliker, S Williams Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States), 2024 | | 2024 |
| AutoCT: Automated CT registration, segmentation, and quantification Z Bai, A Essiari, T Perciano, KE Bouchard SoftwareX 26, 101673, 2024 | | 2024 |
| Data for" Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches" Á Sánchez-Villar, Z Bai, N Bertelli, EW Bethel, J Hillairet, T Perciano, ... Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States), 2024 | | 2024 |
| Overview and findings of the FES Scientific Machine Learning project," Accelerating radio frequency modeling using machine learning" J Wright, Z Bai, G Wallace, N Bertelli, T Perciano, S Shiraiwa, ... Bulletin of the American Physical Society, 2023 | | 2023 |
| ICRF wave propagation and absorption modelling via machine learning Z Bai, N Bertelli, EW Bethel, T Perciano, S Shiraiwa, G Wallace, JC Wright | | 2023 |
Steven L. BruntonProfessor, University of WashingtonVerified email at uw.edu
