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Title: Parameter-dependent model-blending with multi-expert based machine learning and proxy sites

Abstract

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Inventors:
; ; ; ; ; ; ;
Issue Date:
Research Org.:
International Business Machines Corp., Armonk, NY (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1637838
Patent Number(s):
10592817
Application Number:
14/797,777
Assignee:
International Business Machines Corporation (Armonk, NY)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
DOE Contract Number:  
EE0006017
Resource Type:
Patent
Resource Relation:
Patent File Date: 07/13/2015
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Hamann, Hendrik F., Hwang, Youngdeok, Klein, Levente, Lenchner, Jonathan, Lu, Siyuan, Marianno, Fernando J., Tesauro, Gerald J., and van Kessel, Theodore G. Parameter-dependent model-blending with multi-expert based machine learning and proxy sites. United States: N. p., 2020. Web.
Hamann, Hendrik F., Hwang, Youngdeok, Klein, Levente, Lenchner, Jonathan, Lu, Siyuan, Marianno, Fernando J., Tesauro, Gerald J., & van Kessel, Theodore G. Parameter-dependent model-blending with multi-expert based machine learning and proxy sites. United States.
Hamann, Hendrik F., Hwang, Youngdeok, Klein, Levente, Lenchner, Jonathan, Lu, Siyuan, Marianno, Fernando J., Tesauro, Gerald J., and van Kessel, Theodore G. Tue . "Parameter-dependent model-blending with multi-expert based machine learning and proxy sites". United States. https://www.osti.gov/servlets/purl/1637838.
@article{osti_1637838,
title = {Parameter-dependent model-blending with multi-expert based machine learning and proxy sites},
author = {Hamann, Hendrik F. and Hwang, Youngdeok and Klein, Levente and Lenchner, Jonathan and Lu, Siyuan and Marianno, Fernando J. and Tesauro, Gerald J. and van Kessel, Theodore G.},
abstractNote = {A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {3}
}

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