Results 261 to 270 of about 59,334 (307)

Sparse polynomial surrogates for F-actin networks with compliant crosslinkers. [PDF]

Biomech Model Mechanobiol
Pacheco L, Parente M, Ferreira J.
europepmc   +1 more source

KGR-SKATER: Spatially clustered kernel graph regression for counting processes. [PDF]

PLoS One
Wu J, Peters GW, Franks A.
europepmc   +1 more source

Human airway material characterization via inverse finite element analysis and neural network surrogate. [PDF]

Biomech Model Mechanobiol
Badrou A, Mariano CA, Nelson TM, Eskandari M.   +3 more
europepmc   +1 more source

Analytical Modeling and Data-Driven Uncertainty Analysis of the Vibration Response of Partially Liquid-Filled Rotors Under Lateral Excitation. [PDF]

Materials (Basel)
Sun H, Bai X, Li X, Zhang H, Shao Y, Yuan H.   +5 more
europepmc   +1 more source

A Python surrogate modeling framework with derivatives [PDF]

Advances in Engineering Software, 2019
The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and
Mohamed Amine Bouhlel, John T Hwang, Nathalie Bartoli   +2 more
exaly   +2 more sources

Surrogate modeling: tricks that endured the test of time and some recent developments

Structural and Multidisciplinary Optimization, 2021
International audienceTasks such as analysis, design optimization, and uncertainty quantification can be computationally expensive. Surrogate modeling is often the tool of choice for reducing the burden associated with such data-intensive tasks. However,
Felipe A C Viana, Christian Gogu, Tushar Goel   +2 more
exaly   +2 more sources

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The Surrogate Model

2019
This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
openaire   +1 more source

Protecting SLAs with surrogate models

Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems, 2010
In this paper, we propose the use of surrogate models to avoid or limit violations of the service level agreements (protect SLAs) of enterprise applications executed within virtualized data centers (VDCs).Modern enterprise services are delivered along with service level agreements (SLAs) that formalize the expected quality of service, and define ...
Alessio Gambi, Giovanni Toffetti, Mauro Pezzè   +2 more
openaire   +2 more sources

Surrogate Models for Coupled Microgrids

2019
We consider the operation of coupled microgrids. Each microgrid consists of a number of residential energy systems, each including an energy storage device. The goal is to determine an optimal energy exchange between the microgrids, which results in a two-level optimization problem.
Grundel, S., Sauerteig, P., Worthmann, K.   +2 more
openaire   +2 more sources

Surrogate modeling

Water Resources Research, 1972
To improve the accuracy and completeness of a data base is expensive. Mathematical models and digital computer simulation techniques make a quantitative evaluation of the worth of improving the data base possible by empirical sensitivity analysis. Triangular and log triangular error distributions have been found suitable for Monte Carlo experiments to ...
openaire   +1 more source