Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

Cited by (734)

Items 101–200 of 734

1. Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence
   Chenyue Xie, Jianchun Wang, Ke Li, Chao Ma · 2019 · Physical review. E
2. Embedding domain knowledge for machine learning of complex material systems
   Christopher M. Childs, Newell R. Washburn · 2019 · MRS Communications
3. The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling
   Matheus Altomare Cruz, Roney Leon Thompson, Luiz Eduardo Bittencourt Sampaio, Raphael David Aquilino Bacchi · 2019 · Computers & Fluids
4. Using Physics-Informed neural networks for solving Navier-Stokes equations in fluid dynamic complex scenarios
   Tommaso Botarelli, Marco Fanfani, Paolo Nesi, Lorenzo Pinelli · 2025 · Engineering Applications of Artificial Intelligence
5. Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields Using Convolutional Neural Networks
   Carlos A. Michelén Ströfer, Jinlong Wu, Heng Xiao, Eric G. Paterson · 2018 · Communications in Computational Physics
6. Machine learning-based e-commerce platform repurchase customer prediction model
   Cheng-Ju Liu, Tien-Shou Huang, Ping-Tsan Ho, Jui-Chan Huang, Ching-Tang Hsieh · 2020 · PLoS ONE · Retracted
7. Flexible cylinder flow-induced vibration
   Leixin Ma, Ke Lin, Dixia Fan, Jiasong Wang, Michael S. Triantafyllou · 2022 · Physics of Fluids
8. Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling
   Md. Imran H. Khan, Chanaka Batuwatta-Gamage, Azharul Karim, Yuantong Gu · 2022 · Energies
9. Learning stiff chemical kinetics using extended deep neural operators
   Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee, Bryan T. Susi, George Em Karniadakis · 2023 · Computer Methods in Applied Mechanics and Engineering
10. Towards Integrated Field Inversion and Machine Learning With Embedded Neural Networks for RANS Modeling
    Jonathan R. Holland, James D. Baeder, Karthik Duraisamy · 2019 · AIAA SciTech Forum
11. Improving the k–ω–γ–Ar transition model by the field inversion and machine learning framework
    Muchen Yang, Zhixiang Xiao · 2020 · Physics of Fluids
12. Using machine learning to detect the turbulent region in flow past a circular cylinder
    Binglin Li, Zixuan Yang, Xing Zhang, Guowei He, Bing‐Qing Deng, Lian Shen · 2020 · Journal of Fluid Mechanics
13. Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data
    Zhen Zhang, Xudong Song, Shuran Ye, Yiwei Wang, Chenguang Huang, Yiran An, Yaosong Chen · 2018 · Journal of Hydrodynamics
14. Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods
    Zacharias Marinou Nikolaou, Charalambos Chrysostomou, Luc Vervisch, Stewart Cant · 2019 · Flow Turbulence and Combustion
15. Additive-feature-attribution methods: A review on explainable artificial intelligence for fluid dynamics and heat transfer
    A. Cremades, Sergio Hoyas, Ricardo Vinuesa · 2024 · International Journal of Heat and Fluid Flow
16. Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning
    Jianxun Wang, Junji Huang, Lian Duan, Heng Xiao · 2018 · Theoretical and Computational Fluid Dynamics
17. Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network
    Chenyue Xie, Ke Li, Chao Ma, Jianchun Wang · 2019 · Physical Review Fluids
18. A deep learning framework for hydrogen-fueled turbulent combustion simulation
    Jian An, HanYi Wang, Bing Liu, Kai Hong Luo, Fei Qin, Guo Qiang He · 2020 · International Journal of Hydrogen Energy
19. Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number
    Rachit Gupta, Rajeev K. Jaiman · 2022 · Physics of Fluids
20. Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations
    Salar Taghizadeh, Freddie Witherden, Sharath S Girimaji · 2020 · New Journal of Physics
21. Nonlinear wave evolution with data-driven breaking
    Debbie Eeltink, Hubert Branger, Christopher Luneau, Yuchen He, Amin Chabchoub, Jérôme Kasparian, Ton S. van den Bremer, Themistoklis P. Sapsis · 2022 · Nature Communications
22. Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low-Pressure Turbines
    Harshal D. Akolekar, Jack Weatheritt, Nicholas T. Hutchins, Richard D. Sandberg, Gregory M. Laskowski, Vittorio Michelassi · 2018 · Journal of Turbomachinery
23. Wall-modeled large-eddy simulations of spanwise rotating turbulent channels—Comparing a physics-based approach and a data-based approach
    Xinyi L. D. Huang, Xiang I. A. Yang, Robert Francis Kunz · 2019 · Physics of Fluids
24. Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES
    Yifei Guan, Adam Subel, Ashesh Chattopadhyay, Pedram Hassanzadeh · 2022 · Physica D Nonlinear Phenomena
25. Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow
    Rui Ming Fang, David Sondak, Pavlos Protopapas, Sauro Succi A · 2019 · Journal of Turbulence
26. Machine-learning for turbulence and heat-flux model development: A review of challenges associated with distinct physical phenomena and progress to date
    Richard D. Sandberg, Yaomin Zhao · 2022 · International Journal of Heat and Fluid Flow
27. Machine learning enhancement of manoeuvring prediction for ship Digital Twin using full-scale recordings
    Rasmus E. Nielsen, Dimitrios Papageorgiou, Lazaros Nalpantidis, Bugge T. Jensen, Mogens Blanke · 2022 · Ocean Engineering
28. Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures
    Bernardo P. Brener, Matheus Altomare Cruz, Roney Leon Thompson, R. P. Anjos · 2021 · Journal of Fluid Mechanics
29. A Taxonomic Survey of Physics-Informed Machine Learning
    Preetam Ghosh, Joseph Pateras, Pratip Rana · 2023 · Applied Sciences
30. Bearing prognostics and health management based on hybrid physical mechanism and data models: a systematic review
    Shuo Wang, Liang Yan, Shichang Du, Shanshan Li, Xianmin Chen · 2025 · Measurement Science and Technology
31. A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
    Suraj A. Pawar, Omer San, Adil Rasheed, Prakash Vedula · 2020 · NORA - Norwegian Open Research Archives
32. Helicity Dynamics, Inverse, and Bidirectional Cascades in Fluid and Magnetohydrodynamic Turbulence: A Brief Review
    Annick G. Pouquet, Duane Rosenberg, Julia E. Stawarz, Raffaele Marino · 2019 · Earth and Space Science
33. The Cost-Accuracy Trade-Off in Operator Learning with Neural Networks
    Maarten V. de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, Andrew M. Stuart · 2022 · Journal of Machine Learning
34. Improved deep learning method for accurate flow field reconstruction from sparse data
    Jinsong Zhang, Jian Liu, Zhenwei Huang · 2023 · Ocean Engineering
35. Machine learning for energy-water nexus: challenges and opportunities
    Zaidi, Chandola, Allen, Sanyal, Robert Stewart, Bhaduri, McManamay · 2018 · Big Earth Data
36. Data-driven scalar-flux model development with application to jet in cross flow
    Jack Weatheritt, Yaomin Zhao, Richard D. Sandberg, Satoshi Mizukami, Koichi Tanimoto · 2019 · International Journal of Heat and Mass Transfer
37. Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy
    Yunpeng Wang, Zhijie Li, Zelong Yuan, Wenhui Peng, Tianyuan Liu, Jianchun Wang · 2024 · Physical Review Fluids
38. A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
    Suraj Pawar, Omer San, Adil Rasheed, Prakash Vedula · 2020 · Theoretical and Computational Fluid Dynamics
39. Multifidelity aerodynamic flow field prediction using random forest-based machine learning
    Jethro R. Nagawkar, Leifur Leifsson · 2022 · Aerospace Science and Technology
40. Invariance embedded physics-infused deep neural network-based sub-grid scale models for turbulent flows
    Rikhi Bose, Arunabha Mohan Roy · 2023 · Engineering Applications of Artificial Intelligence
41. Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling
    Jinlong Wu, Rui Sun, Sylvain Laizet, Heng Xiao · 2018 · Computer Methods in Applied Mechanics and Engineering
42. Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media
    Nicholas Lubbers, Animesh Agarwal, Yu Chen, Soyoun Son, Mohamed Z. Mehana, Qinjun Kang, Satish Karra, Christoph Junghans, Timothy Clark Germann, Hari Viswanathan · 2020 · Scientific Reports
43. An iterative machine-learning framework for RANS turbulence modeling
    Weishuo Liu, Jian Cheng Fang, Stefano Rolfo, Charles Moulinec, David R. Emerson · 2021 · International Journal of Heat and Fluid Flow
44. Current gust forecasting techniques, developments and challenges
    Peter F. Sheridan · 2018 · Advances in science and research
45. Learning nonlocal constitutive models with neural networks
    Xu‐Hui Zhou, Jiequn Han, Heng Xiao · 2021 · Computer Methods in Applied Mechanics and Engineering
46. Research on Accurate Prediction of the Container Ship Resistance by RBFNN and Other Machine Learning Algorithms
    Yunfei Yang, Haiwen Tu, Lei Song, Lin Chen, De Yong Xie, Jianglong Sun · 2021 · Journal of Marine Science and Engineering
47. Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows
    Suraj A. Pawar, Omer San · 2021 · Physical Review Fluids
48. Data-driven turbulence modeling in separated flows considering physical mechanism analysis
    Chongyang Yan, Haoran Li, Yufei Zhang, Haixin Chen · 2022 · International Journal of Heat and Fluid Flow
49. Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
    Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata · 2022 · Physica D Nonlinear Phenomena
50. Field inversion for data-augmented RANS modelling in turbomachinery flows
    Andrea Ferrero, Angelo Iollo, Francesco Larocca · 2020 · Computers & Fluids
51. Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control
    Xin‐Yang Liu, Jianxun Wang · 2021 · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
52. A hybrid CFD – Deep learning methodology for improving the accuracy of pressure drop prediction in cyclone separators
    Dang Khoi Le, Joon Yong Yoon · 2022 · Process Safety and Environmental Protection
53. A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers
    Zhiyuan Wang, Weiwei Zhang · 2023 · Physics of Fluids
54. Differentiable hybrid neural modeling for fluid-structure interaction
    Xiantao Fan, Jianxun Wang · 2023 · Journal of Computational Physics
55. Flow field reconstruction and prediction of the two-dimensional cylinder flow using data-driven physics-informed neural network combined with long short-term memory
    Yehao Dou, Xun Han, Pengzhi Lin · 2025 · Engineering Applications of Artificial Intelligence
56. From Deep to Physics-Informed Learning of Turbulence: Diagnostics
    Ryan King, Oliver Hennigh, Arvind T. Mohan, Michael Chertkov · 2018 · arXiv (Cornell University)
57. Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning
    Han Gao, Jianxun Wang, Matthew J. Zahr · 2020 · Physica D Nonlinear Phenomena
58. An improved data-free surrogate model for solving partial differential equations using deep neural networks
    Xinhai Chen, Rongliang Chen, Qian Wan, Rui Xu, Jie Liu · 2021 · Scientific Reports
59. Machine learning and numerical investigation on drag reduction of underwater serial multi-projectiles
    Xi Huang, Cheng Cheng, Xiaobing Zhang · 2021 · Defence Technology
60. Predictability of mechanical behavior of additively manufactured particulate composites using machine learning and data-driven approaches
    Steven Malley, Crystal Reina, Somer M. Nacy, Jérôme Gilles, Behrad Koohbor, George Youssef · 2022 · Computers in Industry
61. Zonal Eddy Viscosity Models Based on Machine Learning
    Racheet Matai, Paul A. Durbin · 2019 · Flow Turbulence and Combustion
62. Artificial neural network-based spatial gradient models for large-eddy simulation of turbulence
    Yunpeng Wang, Zelong Yuan, Chenyue Xie, Jianchun Wang · 2021 · AIP Advances
63. Machine Learning for Optical Scanning Probe Nanoscopy
    Xinzhong Chen, Suheng Xu, Sara Shabani, Yueqi Zhao, Matthew Fu, Andrew J. Millis, M. M. Fogler, Abhay N. Pasupathy, Mengkun K. Liu, D. N. Basov · 2022 · Advanced Materials
64. Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation
    Han Gao, Xu Han, Xiantao Fan, Luning Sun, Liping Liu, Lian Duan, Jianxun Wang · 2024 · Computer Methods in Applied Mechanics and Engineering
65. Physics-Informed Artificial Neural Network Approach for Axial Compression Buckling Analysis of Thin-Walled Cylinder
    Fei Tao, Xin Liu, Haodong Du, Wenbin Yu · 2020 · AIAA Journal
66. Digital rheometer twins: Learning the hidden rheology of complex fluids through rheology-informed graph neural networks
    Mohammadamin Mahmoudabadbozchelou, Krutarth M. Kamani, Simon A. Rogers, Safa Jamali · 2022 · Proceedings of the National Academy of Sciences
67. Generalizable physics-constrained modeling using learning and inference assisted by feature-space engineering
    Vishal Srivastava, Karthik Duraisamy · 2021 · Physical Review Fluids
68. On the explainability of machine-learning-assisted turbulence modeling for transonic flows
    Xiao Gang He, Jianheng Tan, Georgios Rigas, Mehdi Vahdati · 2022 · International Journal of Heat and Fluid Flow
69. Fluid Simulation on Neural Flow Maps
    Yitong Deng, Hong-Xing Yu, Diyang Zhang, Jiajun Wu, Bo Zhu · 2023 · ACM Transactions on Graphics
70. Scientific machine learning for closure models in multiscale problems: A review
    Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed · 2024 · Foundations of Data Science
71. CoPhy -PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems
    Mohannad Elhamod, Jie Bu, Christopher N. Singh, Matthew D. Redell, Abantika Ghosh, Viktor A. Podolskiy, Wei‐Cheng Lee, Anuj Karpatne · 2022 · ACM Transactions on Intelligent Systems and Technology
72. Deep structured neural networks for turbulence closure modeling
    Ryley McConkey, Eugene Yee, Fue‐Sang Lien · 2022 · Physics of Fluids
73. Neural Differentiable Modeling with Diffusion-Based Super-Resolution for Two-Dimensional Spatiotemporal Turbulence
    Xiantao Fan, Deepak Akhare, Jianxun Wang · 2024 · Computer Methods in Applied Mechanics and Engineering
74. Turbulence closure modeling with machine learning: a foundational physics perspective
    Sharath S. Girimaji · 2024 · New Journal of Physics
75. Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
    Abhishek Kumar, Syahrir Ridha, Marneni Narahari, Suhaib Umer Ilyas · 2021 · Expert Systems with Applications
76. Data augmented turbulence modeling for three-dimensional separation flows
    Chongyang Yan, Yufei Zhang, Haixin Chen · 2022 · Physics of Fluids
77. Reinforcement-learning-based actuator selection method for active flow control
    Romain Paris, Samir Beneddine, Julien Dandois · 2023 · Journal of Fluid Mechanics
78. Data-driven discovery of turbulent flow equations using physics-informed neural networks
    Shirindokht Yazdani, Mojtaba Tahani · 2024 · Physics of Fluids
79. Epistemic uncertainty quantification for Reynolds-averaged Navier-Stokes modeling of separated flows over streamlined surfaces
    Catherine Gorlé, Stéphanie Zeoli, Michael A. Emory, Johan Larsson, Gianluca Iaccarino · 2019 · Physics of Fluids
80. Flow Pattern Transition in Pipes Using Data-Driven and Physics-Informed Machine Learning
    André Mendes Quintino, Davi Lotfi Lavor Navarro da Rocha, Roberto da Fonseca, Oscar Mauricio Hernández Rodríguez · 2020 · Journal of Fluids Engineering
81. Data-driven RANS closures for wind turbine wakes under neutral conditions
    Julia Steiner, Richard P. Dwight, Axelle Viré · 2021 · Computers & Fluids
82. Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets
    Christian Soize, Roger Ghanem · 2021 · Computer Methods in Applied Mechanics and Engineering
83. Data‐driven modeling of mesoscale solids stress closures for filtered two‐fluid model in gas–particle flows
    Bo Ouyang, Li‐Tao Zhu, Zheng‐Hong Luo · 2021 · AIChE Journal
84. High Reynolds number airfoil turbulence modeling method based on machine learning technique
    Xuxiang Sun, Wenbo Cao, Yilang Liu, Linyang Zhu, Weiwei Zhang · 2021 · Computers & Fluids
85. Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series “Clouds and Climate”)
    Tom Beucler, Imme Ebert‐Uphoff, Stephan Rasp, Michael S. Pritchard, Pierre Gentine · 2021
86. The Potential of Machine Learning to Enhance Computational Fluid Dynamics.
    Ricardo Vinuesa, Steven L. Brunton · 2021 · arXiv (Cornell University)
87. Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks
    Yang Liu, Rui Hu, Adam R. Kraus, Prasanna Balaprakash, Aleksandr V. Obabko · 2022 · Nuclear Engineering and Design
88. Machine learning for vortex induced vibration in turbulent flow
    Xiaodong Bai, Wei Zhang · 2021 · Computers & Fluids
89. Feature importance in neural networks as a means of interpretation for data-driven turbulence models
    Hannes Mandler, Bernhard Weigand · 2023 · Computers & Fluids
90. Recent progress in augmenting turbulence models with physics-informed machine learning
    Xinlei Zhang, Jinlong Wu, Olivier Coutier-Delgosha, Heng Xiao · 2019 · Journal of Hydrodynamics
91. Data-driven RANS closures for three-dimensional flows around bluff bodies
    Jasper P. Huijing, Richard P. Dwight, Martin Schmelzer · 2021 · Computers & Fluids
92. Assimilation of disparate data for enhanced reconstruction of turbulent mean flows
    Xinlei Zhang, Heng Xiao, Guowei He, Shizhao Wang · 2021 · Computers & Fluids
93. Data-driven model for improving wall-modeled large-eddy simulation of supersonic turbulent flows with separation
    Rozie Zangeneh · 2021 · Physics of Fluids
94. Towards robust and accurate Reynolds-averaged closures for natural convection via multi-objective CFD-driven machine learning
    Xiaowei Xu, Fabian Waschkowski, Andrew S. H. Ooi, Richard D. Sandberg · 2022 · International Journal of Heat and Mass Transfer
95. Data-driven nonlinear K-L turbulent mixing model via gene expression programming method
    Han-song Xie, Yaomin Zhao, Yousheng Zhang · 2022 · Acta Mechanica Sinica
96. Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments
    Yang Liu, Xiaodong Sun, Nam T. Dinh · 2019 · Nuclear Engineering and Design
97. Convolutional neural network for transition modeling based on linear stability theory
    Muhammad I. Zafar, Heng Xiao, Meelan M. Choudhari, Fei Li, Chau‐Lyan Chang, Pedro Paredes, Balaji Shankar Venkatachari · 2020 · Physical Review Fluids
98. Deep learning of the spanwise-averaged Navier–Stokes equations
    Bernat Font, Gabriel David Weymouth, Vinh-Tan Nguyen, Owen R. Tutty · 2021 · Journal of Computational Physics
99. Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network
    Anqing Xuan, Lian Shen · 2023 · Journal of Fluid Mechanics
100. An iterative data-driven turbulence modeling framework based on Reynolds stress representation
     Yuhui Yin, Zhi Shen, Yufei Zhang, Haixin Chen, Song Fu · 2022 · Theoretical and Applied Mechanics Letters