A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain

15 March 2022

Papers citing "A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain"

6 / 6 papers shown

Title
Point-DeepONet: A Deep Operator Network Integrating PointNet for Nonlinear Analysis of Non-Parametric 3D Geometries and Load Conditions Jangseop Park Namwoo Kang AI4CE 3DPC 50 1 0 24 Dec 2024
Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites Zeyu Cao Wenjuan Yao Wei Peng Xiaoya Zhang K. Bao AI4CE 29 10 0 01 Sep 2022
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data Xiaoyu Zhao Zhiqiang Gong Yunyang Zhang Wen Yao Xiaoqian Chen OOD AI4CE 69 90 0 26 Sep 2021
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains Hengjie Wang R. Planas Aparna Chandramowlishwaran Ramin Bostanabad AI4CE 42 61 0 22 Apr 2021
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
FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction Houpu Yao Yi Gao Yongming Liu AI4CE 49 66 0 31 Jan 2020