Zobrazeno 1 - 10
of 282
pro vyhledávání: '"Chang, Yu ‐ Hung"'
Autor:
Chien, Shuo-Chen, Yen, Chia-Ming, Chang, Yu-Hung, Chen, Ying-Erh, Liu, Chia-Chun, Hsiao, Yu-Ping, Yang, Ping-Yen, Lin, Hong-Ming, Yang, Tsung-En, Lu, Xing-Hua, Wu, I-Chien, Hsu, Chih-Cheng, Chiou, Hung-Yi, Chung, Ren-Hua
Publikováno v:
In Computer Methods and Programs in Biomedicine October 2024 255
Autor:
Chang, Yu-Hung, Wang, Xingjian, Zhang, Liwei, Li, Yixing, Mak, Simon, Wu, Chien-Fu J., Yang, Vigor
In the present study, we propose a new surrogate model, called common kernel-smoothed proper orthogonal decomposition (CKSPOD), to efficiently emulate the spatiotemporal evolution of fluid flow dynamics. The proposed surrogate model integrates and ex
Externí odkaz:
http://arxiv.org/abs/2101.08893
Autor:
Chang, Yu-Hung1 (AUTHOR) hung61601@gmail.com, Liu, Chien-Hung1 (AUTHOR), You, Shingchern D.1 (AUTHOR) scyou@ntut.edu.tw
Publikováno v:
Information (2078-2489). Feb2024, Vol. 15 Issue 2, p82. 15p.
Publikováno v:
In Asian Journal of Psychiatry January 2023 79
Akademický článek
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Autor:
Chang, Yu-Hung, Zhang, Liwei, Wang, Xingjian, Yeh, Shiang-Ting, Mak, Simon, Sung, Chih-Li, Wu, C. F. Jeff, Yang, Vigor
This interdisciplinary study, which combines machine learning, statistical methodologies, high-fidelity simulations, and flow physics, demonstrates a new process for building an efficient surrogate model for predicting spatiotemporally evolving flow
Externí odkaz:
http://arxiv.org/abs/1802.08812
Autor:
Chien, Shuo-Chen1 (AUTHOR), Chang, Yu-Hung1 (AUTHOR), Yen, Chia-Ming2 (AUTHOR), Chen, Ying-Erh3 (AUTHOR), Liu, Chia-Chun1 (AUTHOR), Hsiao, Yu-Ping1 (AUTHOR), Yang, Ping-Yen1 (AUTHOR), Lin, Hong-Ming1 (AUTHOR), Lu, Xing-Hua1 (AUTHOR), Wu, I-Chien1 (AUTHOR), Hsu, Chih-Cheng1,2 (AUTHOR), Chiou, Hung-Yi1,4 (AUTHOR), Chung, Ren-Hua1 (AUTHOR) rchung@nhri.edu.tw
Publikováno v:
Cancers. Sep2023, Vol. 15 Issue 18, p4598. 14p.
Autor:
Yeh, Shiang-Ting, Wang, Xingjian, Sung, Chih-Li, Mak, Simon, Chang, Yu-Hung, Zhang, Liwei, Wu, C. F. Jeff, Yang, Vigor
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 model employing a machine-learning technique called kriging, which is com
Externí odkaz:
http://arxiv.org/abs/1709.07841
Autor:
Mak, Simon, Sung, Chih-Li, Wang, Xingjian, Yeh, Shiang-Ting, Chang, Yu-Hung, Joseph, V. Roshan, Yang, Vigor, Wu, C. F. Jeff
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 methodology is needed that combines engineering physics, computer simu
Externí odkaz:
http://arxiv.org/abs/1611.07911
Akademický článek
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