145 citations · 389 across the 30 of their papers we have counts for
7 papers · 1 filter
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…
Structure-preserving Nonlinear Filtering for Continuous and Discontinuous Galerkin Spectral/hp Element Methods
Vidhi Zala, Robert M. Kirby, Akil Narayan
Finite element simulations have been used to solve various partial differential equations (PDEs) that model physical, chemical, and biological phenomena. The resulting discretized…
Towards an Extrinsic, CG-XFEM Approach Based on Hierarchical Enrichments for Modeling Progressive Fracture
M. Keith Ballard, Roman Amici, Varun Shankar +3
We propose an extrinsic, continuous-Galerkin (CG), extended finite element method (XFEM) that generalizes the work of Hansbo and Hansbo to allow multiple Heaviside enrichments with…
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…
A bandit-learning approach to multifidelity approximation
Yiming Xu, Vahid Keshavarzzadeh, Robert M. Kirby +1
Multifidelity approximation is an important technique in scientific computation and simulation. In this paper, we introduce a bandit-learning approach for leveraging data of varyin…
Fast Barycentric-Based Evaluation Over Spectral/hp Elements
Edward Laughton, Vidhi Zala, Akil Narayan +2
As the use of spectral/ element methods, and high-order finite element methods in general, continues to spread, community efforts to create efficient, optimized algorithms asso…
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…