• 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 mixed-integer variables...
  • SO-I

  • 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 SO-I 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 black-box global optimization problems. MATSuMoTo ... MATLAB Surrogate Model Toolbox for computationally expensive, black-box, 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 ... /07-AOAS148> and tree surrogate models...
  • BOCK

  • Referenced in 6 articles [sw32130]
  • transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary...
  • mlrMBO

  • Referenced in 9 articles [sw19214]
  • flexible and comprehensive R toolbox for model-based 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, scikit-learn’s regressors...
  • ELFI

  • Referenced in 4 articles [sw26681]
  • several orders of magnitude by surrogate-modelling 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 surrogate-based 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, Student-t Processes, Random Forests, Gradient...
  • mad-GP

  • 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 open-source ... model discrimination that uses Gaussian process surrogate models to approximate and maximise the divergence between...