2 citations · 3 across the 12 of their papers we have counts for
12 papers
Advancing inverse scattering with surrogate modeling and Bayesian inference for functional inputs
Chih-Li Sung, Yao Song, Ying Hung
Inverse scattering aims to infer information about a hidden object by using the received scattered waves and training data collected from forward mathematical models. Recent advanc…
Stacking designs: designing multi-fidelity computer experiments with target predictive accuracy
Chih-Li Sung, Yi Ji, Simon Mak +2
In an era where scientific experiments can be very costly, multi-fidelity emulators provide a useful tool for cost-efficient predictive scientific computing. For scientific applica…
Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak
Chih-Li Sung
As the coronavirus disease 2019 (COVID-19) has shown profound effects on public health and the economy worldwide, it becomes crucial to assess the impact on the virus transmission…
Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19
Chih-Li Sung, Ying Hung
The estimation of unknown parameters in simulations, also known as calibration, is crucial for practical management of epidemics and prediction of pandemic risk. A simple yet widel…
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…
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…
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…
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…
Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments
Chih-Li Sung, Wenjia Wang, Matthew Plumlee +1
The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatl…
A generalized Gaussian process model for computer experiments with binary time series
Chih-Li Sung, Ying Hung, William Rittase +2
Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a genera…
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…