Deep learning at scale for subgrid modeling in turbulent flows

Deep learning at scale for subgrid modeling in turbulent flows

1 October 2019

Papers citing "Deep learning at scale for subgrid modeling in turbulent flows"

8 / 8 papers shown

Title
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations Rajat Kumar Sarkar Ritam Majumdar Vishal Sudam Jadhav Sakhinana Sagar Srinivas Venkataramana Runkana AI4CE DiffM 29 2 0 16 Nov 2023
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows Kai Fukami K. Fukagata Kunihiko Taira AI4CE 19 104 0 26 Jan 2023
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow Mathis Bode AI4CE 22 3 0 28 Oct 2022
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors Mathis Bode AI4CE 27 4 0 28 Oct 2022
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data Mathis Bode M. Gauding Dominik Goeb Tobias Falkenstein H. Pitsch AI4CE 30 17 0 28 Oct 2022
Automated Dissipation Control for Turbulence Simulation with Shell Models Ann-Kathrin Dombrowski Klaus-Robert Muller W. Müller AI4CE 21 0 0 07 Jan 2022
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data Pranshu Pant A. Farimani AI4CE 13 12 0 21 Oct 2020
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows Mathis Bode M. Gauding Zeyu Lian D. Denker M. Davidovic K. Kleinheinz J. Jitsev H. Pitsch AI4CE 14 21 0 26 Nov 2019