28 citations · 39 across the 10 of their papers we have counts for
16 papers
Particle Merging-and-Splitting
Nghia Truong, Cem Yuksel, Chakrit Watcharopas +2
Robustly handling collisions between individual particles in a large particle-based simulation has been a challenging problem. We introduce particle merging-and-splitting, a simple…
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations
Wei W. Xing, Akeel A. Shah, Peng Wang +2
Challenges in multi-fidelity modeling relate to accuracy, uncertainty estimation and high-dimensionality. A novel additive structure is introduced in which the highest fidelity sol…
Kernel optimization for Low-Rank Multi-Fidelity Algorithms
Mani Razi, Robert M. Kirby, Akil Narayan
One of the major challenges for low-rank multi-fidelity (MF) approaches is the assumption that low-fidelity (LF) and high-fidelity (HF) models admit "similar" low-rank kernel repre…
Visualization of topology optimization designs with representative subset selection
Daniel J Perry, Vahid Keshavarzzadeh, Shireen Y Elhabian +3
An important new trend in additive manufacturing is the use of optimization to automatically design industrial objects, such as beams, rudders or wings. Topology optimization, as i…
Deep Multi-Fidelity Active Learning of High-dimensional Outputs
Shibo Li, Robert M. Kirby, Shandian Zhe
Many applications, such as in physical simulation and engineering design, demand we estimate functions with high-dimensional outputs. The training examples can be collected with di…
Structure-preserving function approximation via convex optimization
Vidhi Zala, Robert M. Kirby, Akil Narayan
Approximations of functions with finite data often do not respect certain "structural" properties of the functions. For example, if a given function is non-negative, a polynomial a…