6 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…
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…