24 citations · 26 across the 4 of their papers we have counts for
4 papers · 1 filter
Manifold Learning with Implicit Physics Embedding for Reduced-Order Flow-Field Modeling
Weiji Wang, Chunlin Gong, Xuyi Jia +1
Nonlinear manifold learning (ML) based reduced-order models (ROMs) can substantially improve the quality of nonlinear flow-field modeling. However, noise and the lack of physical i…
A Kernel Ridge Regression Combining Nonlinear ROMs for Accurate Flow Field Reconstruction with Discontinuities
Weiji Wang, Chunlin Gong, Xuyi Jia +1
Nonlinear reduced-order models (ROMs), represented by manifold learning (ML), can effectively improve the modeling accuracy of nonlinear flow fields with discontinuities. However,…
Unsteady aerodynamic prediction using limited samples based on transfer learning
Wen Ji, Xueyuan Sun, Chunna Li +3
In this study, a method for predicting unsteady aerodynamic forces under different initial conditions using a limited number of samples based on transfer learning is proposed, aimi…
An accuracy-enhanced transonic flow prediction method fusing deep learning and reduced-order model
Xuyi Jia, Chunlin Gong, Wen Ji +1
It's difficult to accurately predict the flow with shock waves over an aircraft due to the flow's strongly nonlinear characteristics. In this study, we propose an accuracy-enhanced…