The transformative potential of machine learning for experiments in fluid mechanics

28 March 2023

Ricardo Vinuesa

Steven L. Brunton

B. McKeon

AI4CE

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Papers citing "The transformative potential of machine learning for experiments in fluid mechanics"

20 / 20 papers shown

Title
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning David Ramos Lucas Lacasa E. Valero G. Rubio AI4CE 25 0 0 05 May 2025
A Status Quo Investigation of Large Language Models towards Cost-Effective CFD Automation with OpenFOAMGPT: ChatGPT vs. Qwen vs. Deepseek Wenkang Wang Ran Xu Jingsen Feng Qingfu Zhang Xu Chu 30 2 0 02 Apr 2025
Towards certification: A complete statistical validation pipeline for supervised learning in industry Lucas Lacasa Abel Pardo Pablo Arbelo Miguel Sánchez Pablo Yeste ... Alejandro Martínez-Cava G. Rubio Ignacio Gómez E. Valero Javier de Vicente AI4CE 26 1 0 04 Nov 2024
Modern, Efficient, and Differentiable Transport Equation Models using JAX: Applications to Population Balance Equations Mohammed Alsubeihi Arthur Jessop Ben Moseley Cláudio P. Fonte Ashwin Kumar Rajagopalan 21 0 0 01 Nov 2024
Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions Cong Wang Aoming Liang Fei Han Xinyu Zeng Zhibin Li Dixia Fan Jens Kober AI4CE 20 1 0 01 Oct 2024
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer Andres Cremades S. Hoyas Ricardo Vinuesa FAtt 19 9 0 18 Sep 2024
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models Matteo Tomasetto Andrea Manzoni Francesco Braghin AI4CE 13 1 0 09 Sep 2024
EngineBench: Flow Reconstruction in the Transparent Combustion Chamber III Optical Engine Samuel J. Baker Michael A. Hobley Isabel Scherl Xiaohang Fang Felix C. P. Leach M. Davy AI4CE 21 0 0 05 Jun 2024
Prediction of flow and elastic stresses in a viscoelastic turbulent channel flow using convolutional neural networks Arivazhagan G. Balasubramanian Ricardo Vinuesa O. Tammisola 16 0 0 22 Apr 2024
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective Sunwoong Yang Hojin Kim Y. Hong K. Yee R. Maulik Namwoo Kang PINN AI4CE 18 17 0 05 Jan 2024
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws Ning Liu Yiming Fan Xianyi Zeng Milan Klöwer Lu Zhang Yue Yu AI4CE 13 8 0 18 Dec 2023
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements Esther Lagemann Steven L. Brunton Christian Lagemann 19 4 0 17 Oct 2023
Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems Ivica Kicic Pantelis R. Vlachas G. Arampatzis Michail Chatzimanolakis Leonidas J. Guibas P. Koumoutsakos AI4CE 22 6 0 04 Apr 2023
Identifying regions of importance in wall-bounded turbulence through explainable deep learning Andres Cremades S. Hoyas R. Deshpande Pedro Quintero Martin Lellep ... J. Monty Nicholas Hutchins M. Linkmann I. Marusic Ricardo Vinuesa FAtt 8 26 0 02 Feb 2023
Emerging trends in machine learning for computational fluid dynamics Ricardo Vinuesa Steve Brunton AI4CE 6 14 0 28 Nov 2022
Enhancing Computational Fluid Dynamics with Machine Learning Ricardo Vinuesa Steven L. Brunton AI4CE 98 353 0 05 Oct 2021
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning Kai Fukami R. Maulik Nesar Ramachandra K. Fukagata Kunihiko Taira 32 140 0 03 Jan 2021
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale Michael G. Kapteyn Jacob V. R. Pretorius Karen E. Willcox 32 213 0 10 Dec 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 121 419 0 10 Mar 2020
Learning Symbolic Physics with Graph Networks M. Cranmer Rui Xu Peter W. Battaglia S. Ho PINN AI4CE 177 83 0 12 Sep 2019