Physics-aware Reduced-order Modeling of Transonic Flow via $β$ -Variational Autoencoder

2 May 2022

Papers citing "Physics-aware Reduced-order Modeling of Transonic Flow via $β$-Variational Autoencoder"

8 / 8 papers shown

Title
Towards aerodynamic surrogate modeling based on $β$ -variational autoencoders Víctor Francés-Belda Alberto Solera-Rico Javier Nieto-Centenero Esther Andrés Carlos Sanmiguel Vila Rodrigo Castellanos AI4CE 19 0 0 09 Aug 2024
Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction Sunwoong Yang Ricardo Vinuesa Namwoo Kang AI4CE 49 4 0 06 Jun 2024
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective Sunwoong Yang Hojin Kim Y. Hong K. Yee R. Maulik Namwoo Kang PINN AI4CE 26 17 0 05 Jan 2024
Compact and Intuitive Airfoil Parameterization Method through Physics-aware Variational Autoencoder Yu-Eop Kang Dawoon Lee K. Yee 19 0 0 18 Nov 2023
Data-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder Sihun Lee Sangmin Lee Ki-Hyun Jang Haeseong Cho Sang-Joon Shin 13 6 0 10 Jul 2023
Physics-Assisted Reduced-Order Modeling for Identifying Dominant Features of Transonic Buffet Jing Wang Hairun Xie Miao Zhang Hui Xu 16 9 0 23 May 2023
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task Sunwoong Yang K. Yee UQCV 32 5 0 28 Mar 2023
A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling A. Gruber M. Gunzburger L. Ju Zhu Wang GNN 35 62 0 05 Oct 2021