Articles Citing

Deep-learning-based assessment of skin friction in wall-bounded turbulence

Sergio Hoyas ^* and Nils Benedikt ^†

Andres Cremades and Ricardo Vinuesa ^‡

Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Valencia 46022, Spain

FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden

^*Contact author: serhocal@mot.upv.es
^†Present address: Technical University of Darmstadt, Chair of Fluid Dynamics, Otto-Bernd-Straße 2, 64287 Darmstadt, Germany.
^‡Contact author: rvinuesa@mech.kth.se

The role of turbulence in climate mitigation and sustainable engineering

Sergio Hoyas and Ricardo Vinuesa

Results in Engineering 30 110666 (2026)

On the use of explainable deep learning to assess the bubble-induced turbulence model for multi-phase flows

Jaume Luis-Gómez, Andrés Cremades, Raúl Martínez-Cuenca, Sergio Chiva, and Ricardo Vinuesa

Physics of Fluids 38 043342 (2026)

A multigrid-based super-resolution convolutional neural network for multi-physical fields

Tieying Li and Changfu You

Physics of Fluids 38 055115 (2026)

Computational mechanics empowered by artificial intelligence

Francisco Chinesta, Emmanuel Baranger, Clément Jailin, Anaïs Barasinski, Paola Cinnella, and Frédéric Feyel

Mechanics & Industry 27 26 (2026)

Compact Finite Difference Schemes: A Review of Fundamentals, Applications, and Practical Implementation

Andrea Arroyo Ramo, J. Alberto Conejero, María Jezabel Perez-Quiles, and Sergio Hoyas

Mathematics 14 1958 (2026)

Perspectives on explainable reinforcement learning (XRL) for hydraulic systems

Andres Cremades, Sergio Hoyas, and Ricardo Vinuesa

Journal of Hydraulic Research 64 317 (2026)

Convolutional causal learning for aerodynamic flows

Ryo Koshikawa, Ryo Araki, Qiong Liu, and Kai Fukami

J. Fluid Mech. 1037 A6 (2026)

Prediction of the drag, lift and torque coefficients of non-spherical particles constrained by wall

Shuo Cheng, Chenghuan He, Jianzhi Yang, Chenyue Xie, Nan-Sheng Liu, and Xi-Yun Lu

J. Fluid Mech. 1034 A40 (2026)

Assessment of non-intrusive sensing in wall-bounded turbulence through explainable deep learning

Andrés Cremades, Reinis Freibergs, Sergio Hoyas, Andrea Ianiro, Stefano Discetti, and Ricardo Vinuesa

J. Fluid Mech. 1028 A19 (2026)

Turbulence anisotropy modulation in bubble-laden channel flow: A numerical study

Davide Procacci, Arturo A. Arosemena, Simone Di Giorgio, and Jannike Solsvik

International Journal of Multiphase Flow 195 105505 (2026)

From Black-Box Optimization to Importance-Guided Control: A Perspective on Explainable Deep Reinforcement Learning for Drag Reduction

Belén Reverte-Badillo, Clara Trillo-Yagüe, Andrés Cremades, Ricardo Vinuesa, and Sergio Hoyas

Fluids 11 131 (2026)

Machine Learning-based Reduced-Order Model and multi-fidelity approaches for field reconstruction in CFD: A review

Haibin Wu, Xiaojing Wang, Wei Su, Suli Yang, and Zhengyang Li

Chemical Engineering Science 322 123156 (2026)

Spatiotemporal turbulence generation for downscaling simulations via super-resolution deep learning

Haokai Wu, Yuxin Zhang, Kai Zhang, Shujin Laima, Dai Zhou, and Yong Cao

Applied Mathematical Modelling 156 116901 (2026)

No title available

James Henry and Romit Maulik

Fluid–solid–acoustic interactions of underwater vehicles: A review

Xuan Wang, Shaoqiong Yang, Chao Wang, Meng Yuan, Jiayue Liu, Chao Sun, and Qindong Sun

Physics of Fluids 37 091302 (2025)

Select page:

Physical Review Fluids

Filter

Articles Citing

Deep-learning-based assessment of skin friction in wall-bounded turbulence

The role of turbulence in climate mitigation and sustainable engineering

On the use of explainable deep learning to assess the bubble-induced turbulence model for multi-phase flows

A multigrid-based super-resolution convolutional neural network for multi-physical fields

Computational mechanics empowered by artificial intelligence

Compact Finite Difference Schemes: A Review of Fundamentals, Applications, and Practical Implementation

Perspectives on explainable reinforcement learning (XRL) for hydraulic systems

Convolutional causal learning for aerodynamic flows

Prediction of the drag, lift and torque coefficients of non-spherical particles constrained by wall

Assessment of non-intrusive sensing in wall-bounded turbulence through explainable deep learning

Turbulence anisotropy modulation in bubble-laden channel flow: A numerical study

From Black-Box Optimization to Importance-Guided Control: A Perspective on Explainable Deep Reinforcement Learning for Drag Reduction

Machine Learning-based Reduced-Order Model and multi-fidelity approaches for field reconstruction in CFD: A review

Spatiotemporal turbulence generation for downscaling simulations via super-resolution deep learning

No title available

Fluid–solid–acoustic interactions of underwater vehicles: A review

Filter

Filter

Article Lookup

Enter a citation