Results 61 to 70 of about 4,909,083 (351)

Exploratory Landscape Validation for Bayesian Optimization Algorithms

Mathematics
Bayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of ...
Taleh Agasiev, Anatoly Karpenko
doaj   +1 more source

HAD-BO: A history-aware dynamic Bayesian optimization strategy and its applications in laser-driven plasma high-harmonic generation [PDF]

AIP Advances
An enhanced Bayesian optimization method, named History-Aware Dynamic Bayesian Optimization (HAD-BO), is proposed and applied to optimize the ellipticity in laser-driven plasma surface high-harmonic generation (SHHG).
Ziwei Wang, Chenfei Liao, Sergey Rykovanov, Lili Fan, Jingwei Wang   +4 more
doaj   +1 more source

Bayesian optimization for materials design

, 2015
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets.
A Booker, A Forrester, AB Gelman, AIJ Forrester, B Ankenman, BE Stuckman, CE Rasmussen, D Huang, D Huang, David Ginsbourger, Diana M. Negoescu, DR Jones, HJ Kushner, J Bect, J Knowles, J Mockus, J Villemonteix, J Xie, LP Kaelbling, Noel Cressie, PI Frazier, PI Frazier, PI Frazier, R Waeber, RA Howard, RS Sutton, Sethuraman Sankaran, TJ Santner, W Scott   +28 more
core   +1 more source

Simulation based Bayesian Optimization

Statistics and Computing
Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points through an acquisition function.
Naveiro, Roi, Tang, Becky
openaire   +2 more sources

Pseudo-Bayesian Optimization

, 2023
Bayesian Optimization is a popular approach for optimizing expensive black-box functions. Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of query points that balance exploitation-exploration.
Chen, Haoxian, Lam, Henry
openaire   +2 more sources

Development of graphical user interface for design of experiments via Gaussian process regression and its case study

Science and Technology of Advanced Materials: Methods
Bayesian optimization, coupled with Gaussian process regression and acquisition functions, has proven to be a powerful tool in the field of experimental design.
Yoshiki Hasukawa, Mikael Kuwahara, Lauren Takahashi, Keisuke Takahashi   +3 more
doaj   +1 more source

HYPERPARAMETER OPTIMIZATION BASED ON A PRIORI AND A POSTERIORI KNOWLEDGE ABOUT CLASSIFICATION PROBLEM [PDF]

Научно-технический вестник информационных технологий, механики и оптики, 2020
Subject of Research. The paper deals with Bayesian method for hyperparameter optimization of algorithms, used in machine learning for classification problems.
Valentina S. Smirnova, Viacheslav V. Shalamov, Valeria A. Efimova, Andrey A. Filchenkov   +3 more
doaj   +1 more source

Bayesian $T$-optimal discriminating designs

The Annals of Statistics, 2015
The problem of constructing Bayesian optimal discriminating designs for a class of regression models with respect to the T-optimality criterion introduced by Atkinson and Fedorov (1975a) is considered. It is demonstrated that the discretization of the integral with respect to the prior distribution leads to locally T-optimal discriminating design ...
Dette, Holger, Melas, Viatcheslav B., Guchenko, Roman   +2 more
openaire   +6 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

A Bayesian optimization approach for reliability-based design of prestressed concrete structures

Data-Centric Engineering
This paper presents a reliability-constrained Bayesian optimization framework for structural design under uncertainty, addressing challenges in stochastic optimization where the objectives and constraints are defined implicitly by potentially expensive ...
James Whiteley, Jurgen Becque
doaj   +1 more source