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Surrogate-based optimization has emerged as a cornerstone methodology for tackling expensive engineering design problems by replacing costly high-fidelity simulations or physical experiments with ...

This repository contains code used to perform acoustic parameter estimation using Bayesian optimization with a Gaussian process surrogate model. The following papers use this code: William Jenkins, ...

Abstract: Stopping criteria for Bayesian optimization (BO) automatically terminate the optimization algorithm when a near-optimal solution has likely been reached, avoiding unnecessary expenditure of ...

Muons have broad applications in fundamental science such as material science, chemistry, biology, and nuclear physics [1 – 7], which are produced mainly through the proton-nucleon reactions driven by ...

Abstract: In microwave design, Bayesian optimization (BO) techniques have been widely applied to the optimization of the frequency response of components and devices. The common approach in BO is to ...

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material ...

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of ...

The Python Surrogate Optimization Toolbox (pySOT) is an asynchronous parallel optimization toolbox for computationally expensive global optimization problems. pySOT is built on top of the Plumbing for ...