activity
20172019
most citedData-Driven Analysis and Common Proper Orthogonal Decomposition (CPOD)-Based Spatio-Temporal Emulator for Design Exploration

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

stat.ME2019

A clustered Gaussian process model for computer experiments

Chih-Li Sung, Benjamin Haaland, Youngdeok Hwang +1

A Gaussian process has been one of the important approaches for emulating computer simulations. However, the stationarity assumption for a Gaussian process and the intractability f…

stat.AP2019

Calibration of inexact computer models with heteroscedastic errors

Chih-Li Sung, Beau David Barber, Berkley J. Walker

Computer models are commonly used to represent a wide range of real systems, but they often involve some unknown parameters. Estimating the parameters by collecting physical data b…

stat.ME2018

Calibration for computer experiments with binary responses and application to cell adhesion study

Chih-Li Sung, Ying Hung, William Rittase +2

Calibration refers to the estimation of unknown parameters which are present in computer experiments but not available in physical experiments. An accurate estimation of these para…

cs.CE2018

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…

cs.CE20171 cited

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…