1 citations · 2 across the 4 of their papers we have counts for
4 papers
Common kernel-smoothed proper orthogonal decomposition (CKSPOD): An efficient reduced-order model for emulation of spatiotemporally evolving flow dynamics
Yu-Hung Chang, Xingjian Wang, Liwei Zhang +4
In the present study, we propose a new surrogate model, called common kernel-smoothed proper orthogonal decomposition (CKSPOD), to efficiently emulate the spatiotemporal evolution…
Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamics
Yu-Hung Chang, Liwei Zhang, Xingjian Wang +5
This interdisciplinary study, which combines machine learning, statistical methodologies, high-fidelity simulations, and flow physics, demonstrates a new process for building an ef…
Data-Driven Analysis and Common Proper Orthogonal Decomposition (CPOD)-Based Spatio-Temporal Emulator for Design Exploration
Shiang-Ting Yeh, Xingjian Wang, Chih-Li Sung +5
The present study proposes a data-driven framework trained with high-fidelity simulation results to facilitate decision making for combustor designs. At its core is a surrogate mod…
An efficient surrogate model for emulation and physics extraction of large eddy simulations
Simon Mak, Chih-Li Sung, Xingjian Wang +5
In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design…