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 …
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 …
[图书][B] Advanced reduced order methods and applications in computational fluid dynamics
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 …
From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization
KM Choromanski, A Pacchiano… - Advances in …, 2019 - proceedings.neurips.cc
We present a new algorithm (ASEBO) for optimizing high-dimensional blackbox functions.
ASEBO adapts to the geometry of the function and learns optimal sets of sensing directions …
ASEBO adapts to the geometry of the function and learns optimal sets of sensing directions …
A multifidelity approach coupling parameter space reduction and nonintrusive POD with application to structural optimization of passenger ship hulls
Nowadays, the shipbuilding industry is facing a radical change toward solutions with a
smaller environmental impact. This can be achieved with low emissions engines, optimized …
smaller environmental impact. This can be achieved with low emissions engines, optimized …
A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems
In this work, we present an extension of genetic algorithm (GA) which exploits the
supervised learning technique called active subspaces (AS) to evolve the individuals on a …
supervised learning technique called active subspaces (AS) to evolve the individuals on a …
Learning nonlinear level sets for dimensionality reduction in function approximation
Abstract We developed a Nonlinear Level-set Learning (NLL) method for dimensionality
reduction in high-dimensional function approximation with small data. This work is motivated …
reduction in high-dimensional function approximation with small data. This work is motivated …
Temporal-spatial neighborhood enhanced sparse autoencoder for nonlinear dynamic process monitoring
N Li, H Shi, B Song, Y Tao - Processes, 2020 - mdpi.com
Data-based process monitoring methods have received tremendous attention in recent
years, and modern industrial process data often exhibit dynamic and nonlinear …
years, and modern industrial process data often exhibit dynamic and nonlinear …
Accelerated manifold embedding for multi-view semi-supervised classification
S Wang, Z Wang, W Guo - Information Sciences, 2021 - Elsevier
Multi-view semi-supervised learning has gained much attention since a great number of
unlabeled multi-view data are easy to obtain while few labeled data are available …
unlabeled multi-view data are easy to obtain while few labeled data are available …
Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling
YH Yeung, R Tipireddy, DA Barajas-Solano… - Computer Methods in …, 2024 - Elsevier
We propose a methodology for improving the accuracy of surrogate models of the
observable response of physical systems as a function of the systems' spatially …
observable response of physical systems as a function of the systems' spatially …