6 citations · 6 across the 8 of their papers we have counts for
5 papers · 1 filter
A Constrained Multi-Fidelity Bayesian Optimization Method
Jingyi Wang, Nai-Yuan Chiang, Tucker Hartland +3
Recently, multi-fidelity Bayesian optimization (MFBO) has been successfully applied to many engineering design optimization problems, where the cost of high-fidelity simulations an…
Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization
Socratis Petrides, Tucker Hartland, Tzanio Kolev +6
Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by mi…
Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels
Nick Alger, Tucker Hartland, Noemi Petra +1
We present an efficient matrix-free point spread function (PSF) method for approximating operators that have locally supported non-negative integral kernels. The method computes im…
Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initializaiton
Tucker Hartland, Georg Stadler, Mauro Perego +2
Obtaining lightweight and accurate approximations of Hessian applies in inverse problems governed by partial differential equations (PDEs) is an essential task to make both determi…
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models
Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer +1
Multilevel Stein variational gradient descent is a method for particle-based variational inference that leverages hierarchies of surrogate target distributions with varying costs a…