| 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 | 758 | 2017 |
| 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 | 615 | 2018 |
| 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 | 446 | 2021 |
| 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 | 360 | 2016 |
| Seeing permeability from images: fast prediction with convolutional neural networks JL Wu, X Yin, H Xiao Science Bulletin 63 (18), 1215-1222, 2018 | 213 | 2018 |
| 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 | 183 | 2019 |
| 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 | 160 | 2020 |
| 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 | 117 | 2020 |
| 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 | 112* | 2017 |
| 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 | 94 | 2017 |
| 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 | 92 | 2019 |
| 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 | 81 | 2016 |
| 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 | 54 | 2019 |
| 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 | 52* | 2022 |
| Learning stochastic closures using ensemble Kalman inversion T Schneider, AM Stuart, JL Wu Transactions of Mathematics and Its Applications 5 (1), tnab003, 2021 | 42 | 2021 |
| 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 | 38 | 2017 |
| 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 | 35* | 2021 |
| 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 | 32 | 2019 |
| Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows JL Wu, C Michelén-Ströfer, H Xiao Computers & Fluids 193, 104292, 2019 | 31 | 2019 |
| 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 | 20 | 2016 |
Heng XiaoProfessor, University of StuttgartVerified email at simtech.uni-stuttgart.de
