Jinlong Wu
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Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
JX Wang, JL Wu, H Xiao
Physical Review Fluids 2 (3), 034603, 2017
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach
H Xiao, JL Wu, JX Wang, R Sun, CJ Roy
Journal of Computational Physics 324, 115-136, 2016
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
JL Wu, H Xiao, E Paterson
Physical Review Fluids 3 (7), 074602, 2018
A priori assessment of prediction confidence for data-driven turbulence modeling
JL Wu, JX Wang, H Xiao, J Ling
Flow, Turbulence and Combustion 99 (1), 25-46, 2017
A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations
JL Wu, JX Wang, H Xiao
Flow, Turbulence and Combustion 97 (3), 761-786, 2016
Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks
CM Str÷fer, JL Wu, H Xiao, E Paterson
Communications in Computational Physics 25, 625-650, 2019
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
JL Wu, H Xiao, R Sun, Q Wang
Journal of Fluid Mechanics 869, 553-586, 2019
Seeing permeability from images: fast prediction with convolutional neural networks
JL Wu, X Yin, H Xiao
Science Bulletin 63 (18), 1215-1222, 2018
Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling
JL Wu, R Sun, S Laizet, H Xiao
Computer Methods in Applied Mechanics and Engineering 346, 707-726, 2019
Visualization of High Dimensional Turbulence Simulation Data using t-SNE
JL Wu, JX Wang, H Xiao, J Ling
19th AIAA Non-Deterministic Approaches Conference, 1770, 2017
Physics-informed machine learning for predictive turbulence modeling: toward a complete framework
JX Wang, JL Wu, J Ling, G Iaccarino, H Xiao
Proceedings of the Summer Program, 1, 2016
Incorporating prior knowledge for quantifying and reducing model-form uncertainty in RANS simulations
JX Wang, JL Wu, H Xiao
International Journal for Uncertainty Quantification 6 (2), 2016
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
JL Wu, K Kashinath, A Albert, D Chirila, H Xiao
Journal of Computational Physics 406, 109209, 2020
Physics-informed machine learning for predictive turbulence modeling: Progress and perspectives
H Xiao, JL Wu, JX Wang, EG Paterson
Proceedings of the 2017 AIAA SciTech, 2017
A Physics-Informed Machine Learning Approach of Improving RANS Predicted Reynolds Stresses
JX Wang, JL Wu, H Xiao
55th AIAA Aerospace Sciences Meeting, 1712, 2017
Quantifying Model Form Uncertainty in RANS Simulation of Wing-Body Junction Flow
JL Wu, JX Wang, H Xiao
arXiv preprint arXiv:1605.05962, 2016
Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows
JL Wu, C MichelÚn-Str÷fer, H Xiao
Computers & Fluids 193, 104292, 2019
Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations
H Xiao, JL Wu, S Laizet, L Duan
Computers & Fluids, 104431, 2020
Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
Z Yang, JL Wu, H Xiao
arXiv preprint arXiv:1911.06671, 2019
Deep Learning Recognizes Climate and Weather Patterns and Emulates Complex Processes Critical to the Modeling of Earth's Climate
K Kashinath, M Prabhat, M Mudigonda, A Mahesh, S Kim, J Wu, A Albert, ...
99th American Meteorological Society Annual Meeting, 2019
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