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US Patent 8065244 Neural-network based surrogate model construction methods and applications thereof

Patent 8065244 was granted and assigned to Halliburton on November, 2011 by the United States Patent and Trademark Office.

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Current Assignee
Halliburton
Halliburton
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
8065244
Patent Inventor Names
Allan Zhong2
Dingding Chen2
Stanley Stephenson2
Syed Hamid2
Date of Patent
November 22, 2011
Patent Application Number
12048045
Date Filed
March 13, 2008
Patent Citations Received
‌
US Patent 11775841 Process and system including explainable prescriptions through surrogate-assisted evolution
‌
US Patent 11934956 Regularizing machine learning models
1
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US Patent 11783195 Process and system including an optimization engine with evolutionary surrogate-assisted prescriptions
‌
US Patent 11783099 Autonomous surrogate model creation platform
Patent Primary Examiner
‌
Omar Fernandez Rivas
Patent abstract

Various neural-network based surrogate model construction methods are disclosed herein, along with various applications of such models. Designed for use when only a sparse amount of data is available (a “sparse data condition”), some embodiments of the disclosed systems and methods: create a pool of neural networks trained on a first portion of a sparse data set; generate for each of various multi-objective functions a set of neural network ensembles that minimize the multi-objective function; select a local ensemble from each set of ensembles based on data not included in said first portion of said sparse data set; and combine a subset of the local ensembles to form a global ensemble. This approach enables usage of larger candidate pools, multi-stage validation, and a comprehensive performance measure that provides more robust predictions in the voids of parameter space.

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