
DACE
 Referenced in 190 articles
[sw04715]
 this approximation model as a surrogate for the computer model. The software also addresses...

ParEGO
 Referenced in 68 articles
[sw10968]
 hypercube and updates a Gaussian processes surrogate model of the search landscape after every function...

SUMO
 Referenced in 26 articles
[sw12763]
 Surrogate Modeling Toolbox (SUMO Toolbox) is a Matlab toolbox that automatically builds accurate surrogate models...

MISO
 Referenced in 13 articles
[sw20541]
 evaluations. Therefore, we use computationally cheap surrogate models to approximate the expensive objective function ... evaluated. We develop a general surrogate model framework and show how sampling strategies of well ... known surrogate model algorithms for continuous optimization can be modified for mixedinteger variables...

SOI
 Referenced in 15 articles
[sw10100]
 surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications. This paper ... presents the surrogate model based algorithm SOI for solving purely integer optimization problems that...

ooDACE
 Referenced in 10 articles
[sw12876]
 data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators ... optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis...

pySOT
 Referenced in 6 articles
[sw26653]
 methods. We have implemented many different surrogate models, experimental designs, auxiliary problems (also known ... makes it easy to add new surrogate models, experimental designs, and auxiliary problems...

MATSuMoTo
 Referenced in 4 articles
[sw20551]
 MATSuMoTo: The MATLAB surrogate model toolbox for computationally expensive blackbox global optimization problems. MATSuMoTo ... MATLAB Surrogate Model Toolbox for computationally expensive, blackbox, global optimization problems that may have ... function, derivatives are not available. Hence, surrogate models are used as computationally cheap approximations ... challenges. MATSuMoTo offers various choices for surrogate models and surrogate model mixtures, initial experimental design...

RBFOpt
 Referenced in 8 articles
[sw28416]
 which builds and iteratively refines a surrogate model of the unknown objective function...

iml
 Referenced in 7 articles
[sw28966]
 /10618600.2014.907095>, local models (variant of ’lime’) described by Ribeiro et. al (2016) , the Shapley Value ... /07AOAS148> and tree surrogate models...

BOCK
 Referenced in 6 articles
[sw32130]
 transformed geometry, the Gaussian Processbased surrogate model spends less budget searching near the boundary...

mlrMBO
 Referenced in 9 articles
[sw19214]
 flexible and comprehensive R toolbox for modelbased optimization (MBO), also known as Bayesian optimization ... given objective function through a surrogate regression model. It is designed for both single...

BATMAN
 Referenced in 4 articles
[sw29121]
 Batman stands for Bayesian Analysis Tool for Modelling and uncertAinty quaNtification. It is a Python ... physic and the sample, Surrogate Models (Gaussian process, Polynomial Chaos, RBF, scikitlearn’s regressors...

ELFI
 Referenced in 4 articles
[sw26681]
 several orders of magnitude by surrogatemodelling the distance. ELFI also has an inbuilt support...

PINNeik
 Referenced in 3 articles
[sw42324]
 source location, particularly in large 3D models. Here, we propose an algorithm to solve ... learning techniques like transfer learning and surrogate modeling to speed up traveltime computations for updated...

DAKOTA
 Referenced in 77 articles
[sw05202]
 components within advanced strategies such as surrogatebased optimization, mixed integer nonlinear programming, or optimization ... design and performance analysis of computational models on high performance computers...

NoFAS
 Referenced in 1 article
[sw42985]
 NoFAS: normalizing flow with adaptive surrogate for computationally expensive models. Fast inference of numerical model ... true model with an offline trained surrogate model, such as neural networks. However, this approach ... updates the normalizing flow parameters and surrogate model parameters. We also propose an efficient sample ... weighting scheme for surrogate model training that preserves global accuracy while effectively capturing high posterior...

PyGPGO
 Referenced in 2 articles
[sw41576]
 Bayesian optimization. It supports: Different surrogate models: Gaussian Processes, Studentt Processes, Random Forests, Gradient...

madGP
 Referenced in 1 article
[sw42206]
 design space of Gaussian process (GP) surrogate models for modeling potential energy surfaces (PESs ... test the effectiveness of fitting GP surrogates to energies and/or forces, and perform a preliminary ... performing geometry optimization with GP surrogate models on small molecules...

GPdoemd
 Referenced in 1 article
[sw26109]
 design of experiments for model discrimination using Gaussian process surrogates. GPdoemd is an opensource ... model discrimination that uses Gaussian process surrogate models to approximate and maximise the divergence between...