Physics-informed deep-learning applications to experimental fluid
mechanics

Physics-informed deep-learning applications to experimental fluid mechanics

29 March 2022

Hamidreza Eivazi

Ricardo Vinuesa

Papers citing "Physics-informed deep-learning applications to experimental fluid mechanics"

10 / 10 papers shown

Title
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 81 0 0 25 Apr 2025
Prediction of flow and elastic stresses in a viscoelastic turbulent channel flow using convolutional neural networks Arivazhagan G. Balasubramanian Ricardo Vinuesa O. Tammisola 26 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 26 17 0 05 Jan 2024
Unveiling the frontiers of deep learning: innovations shaping diverse domains Shams Forruque Ahmed Md. Sakib Bin Alam Maliha Kabir Shaila Afrin Sabiha Jannat Rafa Aanushka Mehjabin Amir H. Gandomi AI4CE 42 2 0 06 Sep 2023
Physics-Informed Computer Vision: A Review and Perspectives C. Banerjee Kien Nguyen Clinton Fookes G. Karniadakis PINN AI4CE 34 29 0 29 May 2023
The transformative potential of machine learning for experiments in fluid mechanics Ricardo Vinuesa Steven L. Brunton B. McKeon AI4CE 35 68 0 28 Mar 2023
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems Daniel Kelshaw Georgios Rigas Luca Magri AI4CE 37 17 0 31 Oct 2022
Thermodynamics-informed neural networks for physically realistic mixed reality Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto PINN AI4CE 22 16 0 24 Oct 2022
Improving aircraft performance using machine learning: a review S. L. Clainche E. Ferrer Sam Gibson Elisabeth Cross A. Parente Ricardo Vinuesa AI4CE 28 93 0 20 Oct 2022
Enhancing Computational Fluid Dynamics with Machine Learning Ricardo Vinuesa Steven L. Brunton AI4CE 114 355 0 05 Oct 2021