Title
A surrogate modeling and adaptive sampling toolbox for computer based design
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Cambridge, Mass. ,
Subject
Computer. Automation
Source (journal)
Journal of machine learning research. - Cambridge, Mass.
Volume/pages
11(2010) , p. 2051-2055
ISSN
1532-4435
ISI
000282523000004
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle these issues. The toolkit brings together algorithms for data fitting, model selection, sample selection (active learning), hyperparameter optimization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for the problem or data at hand.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/6e6ab4/ce37b493.pdf
E-info
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Handle