A composable machine-learning approach for steady-state simulations on high-resolution grids

11 October 2022

Papers citing "A composable machine-learning approach for steady-state simulations on high-resolution grids"

7 / 7 papers shown

Title
Sampling-based Distributed Training with Message Passing Neural Network P. Kakka Sheel Nidhan Rishikesh Ranade Jay Pathak J. MacArt GNN 75 3 0 20 Feb 2025
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations Sheel Nidhan Haoliang Jiang Lalit Ghule C. Umphrey Rishikesh Ranade Jay Pathak AI4CE 34 0 0 26 Aug 2024
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 69 220 0 26 Apr 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 98 272 0 20 Apr 2021
$NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework$ NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework O. Hennigh S. Narasimhan M. A. Nabian Akshay Subramaniam Kaustubh Tangsali M. Rietmann J. Ferrandis Wonmin Byeon Z. Fang S. Choudhry PINN AI4CE 91 125 0 14 Dec 2020
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,272 0 18 Oct 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 117 506 0 11 Mar 2020