101 citations · 245 across the 14 of their papers we have counts for
15 papers
Scalable, Likelihood-Free Calibration of Ice-Sheet Models with Deep Diffusion Emulators and Feature Matching
Kanghyun Wi, Jaewoo Park, Saumya Bhatnagar +1
The Antarctic ice sheet is a major source of uncertainty in future sea-level projections, and physical simulators such as the PSU3D-ICE model are essential for studying its evoluti…
An adaptive split-combine Gaussian mixture filter for nonlinear and multimodal state estimation
San Kim, Won Chang, Daniel B. Forger +1
Filtering combines model predictions with measurements to estimate the probability density function (PDF) of a system state over time. The PDF often becomes highly asymmetric and e…
Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions
Eunbi Yoon, Won Chang, Donghan Kim +1
Data assimilation is the process of estimating the state of a dynamical system over time by combining model predictions with measurements. This task becomes challenging when the sy…
Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments
Youngdeok Hwang, Hang J. Kim, Won Chang +2
Understanding the oscillating behaviors that govern organisms' internal biological processes requires interdisciplinary efforts combining both biological and computer experiments,…
Fast and accurate learned multiresolution dynamical downscaling for precipitation
Jiali Wang, Zhengchun Liu, Ian Foster +3
This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computati…
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression
Saumya Bhatnagar, Won Chang, Seonjin Kim Jiali Wang
Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiment is input parameter uncertainty. Computer m…