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Zhe Bai
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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
582015
Dynamic mode decomposition for compressive system identification
Z Bai, E Kaiser, JL Proctor, JN Kutz, SL Brunton
AIAA Journal 58 (2), 561-574, 2020
532020
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
382017
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
92019
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
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
72013
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
42021
Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation
M Avaylon, R Sadre, ZBT Perciano
Advances in Artificial Intelligence and Machine Learning 2 (01), 288-302, 2022
2022
Automated CT registration, segmentation, and quantification (AutoCT) v1. 0
A Essiari, Z Bai, TPC Leite, K Bouchard
Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States), 2021
2021
Automated CT registration, segmentation, and quantification (AutoCT) v1. 1
Z Bai, A Essiari, T Perciano Costa Leite, K Bouchard
Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States), 2021
2021
Overview and status of the FES Scientific Machine Learning project,''Accelerating radio frequency modeling using machine learning''
J Wright, G Wallace, W Bethel, Z Bai, T Perciano, R Sadre, N Bertelli, ...
APS Division of Plasma Physics Meeting Abstracts 2021, TM10. 002, 2021
2021
Towards Fast, Accurate Predictions of RF Power Deposition/Current Profile via Data-driven Modeling
G Wallace, J Wright, EW Bethel, Z Bai, T Perciano, R Sadre, S Shiraiwa, ...
APS Division of Plasma Physics Meeting Abstracts 2021, GP11. 020, 2021
2021
Overview on sparsity in fluids
Z Bai, S Brunton
APS Division of Fluid Dynamics Meeting Abstracts, P17. 001, 2019
2019
Sparse and randomized sampling methods for scalable turbulent flow networks
Z Bai, NB Erichson, M Gopalakrishnan Meena, K Taira, S Brunton
Bulletin of the American Physical Society 63, 2018
2018
Sparse Sensing and Modal Decomposition for Unsteady Fluid Flows
Z Bai
2018
Compressed sensing DMD with control
Z Bai, E Kaiser, J Proctor, JN Kutz, S Brunton
APS Division of Fluid Dynamics Meeting Abstracts, L8. 009, 2016
2016
Flow classification using machine learning on sparsely sampled experimental flow visualization data
Z Bai, SL Brunton, BW Brunton, JN Kutz, E Kaiser, A Spohn, BR Noack
APS Division of Fluid Dynamics Meeting Abstracts, M27. 009, 2015
2015
Low-Dimensional Approach for Reconstruction of Airfoil Data via Compressive Sensing
Z Berger, T Wimalajeewa, Z Bai, G Wang, M Glauser, PK Varshney
2015
Wavelet diagnostics of the flow control of unsteady separation on a 2D Wind Turbine Airfoil
Z Bai, J Lewalle, G Wang, M Glauser
APS Division of Fluid Dynamics Meeting Abstracts, H25. 004, 2013
2013
Physics based compressive sensing approach applied to airfoil data collection and analysis
Z Bai, T Wimalajeewa, Z Berger, G Wang, M Glauser, PK Varshney
51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and …, 2013
2013
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