6 citations · 6 across the 8 of their papers we have counts for
9 papers
Practical Efficient Global Optimization is No-regret
Jingyi Wang, Haowei Wang, Nai-Yuan Chiang +3
Efficient global optimization (EGO) is one of the most widely used noise-free Bayesian optimization algorithms.It comprises the Gaussian process (GP) surrogate model and expected i…
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…
A Scalable Interior-Point Gauss-Newton Method for PDE-Constrained Optimization with Bound Constraints
Tucker Hartland, Cosmin G. Petra, Noemi Petra +1
We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a r…
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…
Indecomposable vector-valued modular forms and periods of modular curves
Luca Candelori, Tucker Hartland, Christopher Marks +1
We classify the three-dimensional representations of the modular group that are reducible but indecomposable, and their associated spaces of holomorphic vector-valued modular forms…
A strong maximum principle for nonlinear nonlocal diffusion equations
Ravi Shankar, Tucker Hartland
This is a study of a class of nonlocal nonlinear diffusion equations. We present a strong maximum principle for nonlocal time-dependent Dirichlet problems. Results are for bounded…