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A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
[ספר][B] Advanced reduced order methods and applications in computational fluid dynamics
G Rozza, G Stabile, F Ballarin - 2022 - SIAM
Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …
science and engineering, motivated by several reasons, of which we mention just a few …
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
Optimizing discrete black-box functions is key in several domains, eg protein engineering
and drug design. Due to the lack of gradient information and the need for sample efficiency …
and drug design. Due to the lack of gradient information and the need for sample efficiency …
Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering
Multi‐fidelity models are of great importance due to their capability of fusing information
coming from different numerical simulations, surrogates, and sensors. We focus on the …
coming from different numerical simulations, surrogates, and sensors. We focus on the …
Latent map Gaussian processes for mixed variable metamodeling
Gaussian processes (GPs) are ubiquitously used in sciences and engineering as
metamodels. Standard GPs, however, can only handle numerical or quantitative variables …
metamodels. Standard GPs, however, can only handle numerical or quantitative variables …
Enhancing CFD predictions in shape design problems by model and parameter space reduction
In this work we present an advanced computational pipeline for the approximation and
prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced …
prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced …
Comparison of high-dimensional bayesian optimization algorithms on bbob
Bayesian Optimization (BO) is a class of surrogate-based black-box optimization heuristics
designed to efficiently locate high-quality solutions for problems that are expensive to …
designed to efficiently locate high-quality solutions for problems that are expensive to …
Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data
Background: For successful biomarker discovery, it is essential to develop computational
frameworks that summarize high-dimensional neuroimaging data in terms of involved sub …
frameworks that summarize high-dimensional neuroimaging data in terms of involved sub …
High-dimensional Bayesian Optimization with Group Testing
Bayesian optimization is an effective method for optimizing expensive-to-evaluate black-box
functions. High-dimensional problems are particularly challenging as the surrogate model of …
functions. High-dimensional problems are particularly challenging as the surrogate model of …
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
We consider the problem of active learning for global sensitivity analysis of expensive black-
box functions. Our aim is to efficiently learn the importance of different input variables, eg, in …
box functions. Our aim is to efficiently learn the importance of different input variables, eg, in …