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RESEARCH PAPER · AIAA Aerospace Sciences Meeting · 2017

A Physics-Informed Machine Learning Approach of Improving RANS Predicted Reynolds Stresses

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42 direct citing works

A Physics-Informed Machine Learning Approach of Improving RANS Predicted Reynolds StressesOriginalA data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation41Theory-guided machine learning for damage characterization of composites152Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning52Using deep learning to explore local physical similarity for global-scale bridging in thermal-hydraulic simulation33Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach9Machine learning assisted characterisation and simulation of compressive damage in composite laminates96Traffic Flow Modeling With Gradual Physics Regularized Learning35Research on grid‐dependence of neural network turbulence model3Data-driven turbulence modeling: A mutually coupled framework for symbolic regression and data assimilation9Surrogate Modelling and Simulation Approaches for Renal Artery Haemodynamics: Balancing Symmetry in Computational Cost and Accuracy1

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  1. A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation

    Nuclear Engineering and Design · 2019-04-22

    Han Bao, Nam T. Dinh, Jeffrey W. Lane, Robert Youngblood

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  2. Theory-guided machine learning for damage characterization of composites

    Composite Structures · 2020-04-23

    Navid Zobeiry, Johannes Reiner, Reza Vaziri

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  3. Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

    International Journal of Multiphase Flow · 2020-06-06

    Han Bao, Jinyong Feng, Nam T. Dinh, Hongbin Zhang

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  4. Using deep learning to explore local physical similarity for global-scale bridging in thermal-hydraulic simulation

    Annals of Nuclear Energy · 2020-07-13

    Han Bao, Nam T. Dinh, Linyu Lin, Robert Youngblood, Jeffrey W. Lane, Hongbin Zhang

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  5. Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach

    arXiv (Cornell University) · 2020-07-17

    Yun Long Yuan, Qinzheng Wang, Xianfeng “Terry” Yang

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  6. Machine learning assisted characterisation and simulation of compressive damage in composite laminates

    Composite Structures · 2021-06-21

    Johannes Reiner, Reza Vaziri, Navid Zobeiry

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  7. Traffic Flow Modeling With Gradual Physics Regularized Learning

    IEEE Transactions on Intelligent Transportation Systems · 2021-12-07

    Yun Long Yuan, Qinzheng Wang, Xianfeng “Terry” Yang

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  8. Research on grid‐dependence of neural network turbulence model

    International Journal for Numerical Methods in Fluids · 2022-06-20

    Shangxiao Song, Weiwei Zhang, Xuxiang Sun, Linyang Zhu, Yilang Liu

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  9. Data-driven turbulence modeling: A mutually coupled framework for symbolic regression and data assimilation

    Physics of Fluids · 2025-07-01

    Hui Liao, Xuxiang Sun, Yilang Liu, Weiwei Zhang

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  10. Surrogate Modelling and Simulation Approaches for Renal Artery Haemodynamics: Balancing Symmetry in Computational Cost and Accuracy

    Symmetry · 2025-10-08

    Dávid Csonka, Tamás Storcz, András Kaszás, Árpád Forberger, Géza Várady

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