4 citations · 7 across the 8 of their papers we have counts for
8 papers
Knowledge-guided generative surrogate modeling for high-dimensional design optimization under scarce data
Bingran Wang, Seongha Jeong, Sebastiaan P. C. van Schie +3
Surrogate models are widely used in mechanical design and manufacturing process optimization, where high-fidelity computational models may be unavailable or prohibitively expensive…
Spline Dimensional Decomposition with Interpolation-based Optimal Knot Selection for Stochastic Dynamic Analysis
Yeonsu Kim, Junhan Lee, Bingran Wang +2
Forward uncertainty quantification in dynamical systems is challenging due to non-smooth or locally oscillating nonlinear behaviors. Spline dimensional decomposition (SDD) addresse…
A comparative study of uncertainty quantification methods in gust response analysis of a Lift-Plus-Cruise eVTOL aircraft wing
Bingran Wang, Michael Warner, Aoran Tian +2
Wind gusts, being inherently stochastic, can significantly influence the safety and performance of aircraft. This study investigates a three-dimensional uncertainty quantification…
Design optimization of semiconductor manufacturing equipment using a novel multi-fidelity surrogate modeling approach
Bingran Wang, Min Sung Kim, Taewoong Yoon +4
Careful design of semiconductor manufacturing equipment is crucial for ensuring the performance, yield, and reliability of semiconductor devices. Despite this, numerical optimizati…
Extension of graph-accelerated non-intrusive polynomial chaos to high-dimensional uncertainty quantification through the active subspace method
Bingran Wang, Nicholas C. Orndorff, Mark Sperry +1
The recently introduced graph-accelerated non-intrusive polynomial chaos (NIPC) method has shown effectiveness in solving a broad range of uncertainty quantification (UQ) problems…
A gradient-enhanced univariate dimension reduction method for uncertainty propagation
Bingran Wang, Nicholas C. Orndorff, Mark Sperry +1
The univariate dimension reduction (UDR) method stands as a way to estimate the statistical moments of the output that is effective in a large class of uncertainty quantification (…
Graph-accelerated non-intrusive polynomial chaos expansion using partially tensor-structured quadrature rules for uncertainty quantification
Bingran Wang, Nicholas C. Orndorff, John T. Hwang
Recently, the graph-accelerated non-intrusive polynomial chaos (NIPC) method has been proposed for solving uncertainty quantification (UQ) problems. This method leverages the full-…
Accelerating model evaluations in uncertainty propagation on tensor grids using computational graph transformations
Bingran Wang, Mark Sperry, Victor E. Gandarillas +1
Methods such as non-intrusive polynomial chaos (NIPC), and stochastic collocation are frequently used for uncertainty propagation problems. Particularly for low-dimensional problem…