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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
7132017
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
5742018
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
3422016
Physics-informed machine learning: case studies for weather and climate modelling
K Kashinath, M Mustafa, A Albert, JL Wu, C Jiang, S Esmaeilzadeh, ...
Philosophical Transactions of the Royal Society A 379 (2194), 20200093, 2021
3372021
Seeing permeability from images: fast prediction with convolutional neural networks
JL Wu, X Yin, H Xiao
Science Bulletin 63 (18), 1215-1222, 2018
1892018
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
1672019
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
JL Wu, K Kashinath, A Albert, D Chirila, Prabhat, H Xiao
Journal of Computational Physics 406, 109209, 2020
1502020
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
107*2017
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 200, 104431, 2020
1012020
A comprehensive physics-informed machine learning framework for predictive turbulence modeling
JX Wang, J Wu, J Ling, G Iaccarino, H Xiao
arXiv preprint arXiv:1701.07102, 2017
892017
Data-driven, physics-based feature extraction from fluid flow fields using convolutional neural networks
CM Strofer, JL Wu, H Xiao, E Paterson
Communications in Computational Physics 25 (3), 625-650, 2019
882019
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
772016
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
502019
Ensemble Kalman Inversion for Sparse Learning of Dynamical Systems from Time-Averaged Data
T Schneider, AM Stuart, JL Wu
Journal of Computational Physics 470, 111559, 2022
41*2022
Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems
Y Zeng, JL Wu, H Xiao
Communications in Computational Physics 30 (3), 635-665, 2021
33*2021
Learning stochastic closures using ensemble Kalman inversion
T Schneider, AM Stuart, JL Wu
Transactions of Mathematics and Its Applications 5 (1), tnab003, 2021
332021
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
322017
Recent progress in augmenting turbulence models with physics-informed machine learning
X Zhang, J Wu, O Coutier-Delgosha, H Xiao
Journal of Hydrodynamics 31 (6), 1153-1158, 2019
302019
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
282019
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
202016
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