From Deep to Physics-Informed Learning of Turbulence: Diagnostics

From Deep to Physics-Informed Learning of Turbulence: Diagnostics

16 October 2018

Michael Chertkov

Papers citing "From Deep to Physics-Informed Learning of Turbulence: Diagnostics"

9 / 9 papers shown

Title
Physics-Constrained Generative Adversarial Networks for 3D Turbulence D. Tretiak A. Mohan Daniel Livescu GAN AI4CE PINN 32 2 0 01 Dec 2022
Knowledge-Integrated Informed AI for National Security Anu Myne Kevin J. Leahy Ryan Soklaski 21 0 0 04 Feb 2022
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
Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence M. Woodward Yifeng Tian Criston Hyett Chris L. Fryer Daniel Livescu Mikhail Stepanov Michael Chertkov AI4CE 19 8 0 25 Oct 2021
Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer B. Keith U. Khristenko B. Wohlmuth 25 7 0 23 Jul 2021
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction Filipe de Avila Belbute-Peres T. Economon J. Zico Kolter AI4CE 31 223 0 08 Jul 2020
Reservoir computing model of two-dimensional turbulent convection S. Pandey J. Schumacher 17 40 0 28 Jan 2020
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 34 626 0 29 Mar 2019
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 236 7,906 0 13 Jun 2015