5 citations · 5 across the 6 of their papers we have counts for
6 papers
Robust design optimization for a nonlinear system via Bayesian neural network enhanced polynomial dimensional decomposition
Hyunho Jang, Dongjin Lee
Uncertainties such as manufacturing tolerances cause performance variations in complex engineering systems, making robust design optimization (RDO) essential. However, simulation-b…
Predicting passenger injury distributions under uncertainty variables using Gaussian process modeling with GHBMC
Changmin Baek, Junik Cho, Dongjin Lee
This work presents a Gaussian Process (GP) modeling method to predict statistical characteristics of injury kinematics responses using Human Body Models (HBM) more accurately and e…
Robust Design Optimization with Limited Data for Char Combustion
Yulin Guo, Dongjin Lee, Boris Kramer
This work presents a robust design optimization approach for a char combustion process in a limited-data setting, where simulations of the fluid-solid coupled system are computatio…
Global sensitivity analysis with limited data via sparsity-promoting D-MORPH regression: Application to char combustion
Dongjin Lee, Elle Lavichant, Boris Kramer
In uncertainty quantification, variance-based global sensitivity analysis quantitatively determines the effect of each input random variable on the output by partitioning the total…
Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging
Dongjin Lee, Boris Kramer
We propose novel methods for Conditional Value-at-Risk (CVaR) estimation for nonlinear systems under high-dimensional dependent random inputs. We develop a novel DD-GPCE-Kriging su…
Bi-fidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos expansion
Dongjin Lee, Boris Kramer
Digital twin models allow us to continuously assess the possible risk of damage and failure of a complex system. Yet high-fidelity digital twin models can be computationally expens…