Jian-Xun Wang

Research

Prof. Wang’s research focuses on data-driven/augmented computational modeling, which broadly revolves around physics-informed machine learning, Bayesian data assimilation, and uncertainty quantification. The main idea is to develop accurate physics-based computational models of energy-related systems by leveraging available data from high-fidelity simulations, experiments, and field observation using advanced data assimilation and machine learning techniques. Moreover, he is also interested in quantifying and reducing uncertainties associated with the computational models.

Research Interests

Scientific Machine Learning, Data Assimilation, Bayesian Inference, CFD, Uncertainty Quantification, Optimization, Renewable Energy, Energy Conversion and Efficiency, Smart Distribution and Storage, Transformative Wind

Relevant Energy Publications

1. Wang, Jian-Xun, Jin-Long Wu, and Heng Xiao. "Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data." Physical Review Fluids 2, no. 3 (2017): 034603.
2. Gao, Han, and Jian-Xun Wang. "A Bi-fidelity ensemble kalman method for PDE-constrained inverse problems in computational mechanics." Computational Mechanics 67, no. 4 (2021): 1115-1131.
3. Gao, Han, Luning Sun, and Jian-Xun Wang. "Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels." Physics of Fluids 33, no. 7 (2021): 073603.
4. Liu, Xin-Yang, and Jian-Xun Wang. "Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control." Proceedings of the Royal Society A 477, no. 2255 (2021): 20210618.
5. Xiao, Heng, J-L. Wu, J-X. Wang, Rui Sun, and C. J. Roy. "Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach." Journal of Computational Physics 324 (2016): 115-136.