Turbulence Enrichment using Physics-informed Generative Adversarial Networks

4 March 2020

Papers citing "Turbulence Enrichment using Physics-informed Generative Adversarial Networks"

9 / 9 papers shown

Title
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning Pu Ren N. Benjamin Erichson Shashank Subramanian Omer San Z. Lukić Michael W. Mahoney Michael W. Mahoney 52 13 0 24 Jun 2023
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics Rajat Arora AI4CE 35 10 0 30 Jun 2022
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity Rajat Arora AI4CE 26 7 0 16 Dec 2021
Generative Modeling of Turbulence Claudia Drygala Benjamin Winhart F. Mare Hanno Gottschalk GAN AI4CE 27 38 0 05 Dec 2021
Adversarial sampling of unknown and high-dimensional conditional distributions M. Hassanaly Andrew Glaws Karen Stengel Ryan N. King GAN 27 21 0 08 Nov 2021
Performance and accuracy assessments of an incompressible fluid solver coupled with a deep Convolutional Neural Network Ekhi Ajuria Illarramendi M. Bauerheim B. Cuenot 38 19 0 20 Sep 2021
Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer B. Keith U. Khristenko B. Wohlmuth 33 7 0 23 Jul 2021
Point-Cloud Deep Learning of Porous Media for Permeability Prediction Ali Kashefi T. Mukerji 3DPC AI4CE 22 34 0 18 Jul 2021
Multi-fidelity Generative Deep Learning Turbulent Flows N. Geneva N. Zabaras AI4CE 21 44 0 08 Jun 2020