Neural-network based surrogate model construction methods and applications thereof
US8065244B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 13, 2008 |
| Grant date | Nov 22, 2011 |
| Priority date | — |
| Expiry date | Sep 22, 2030 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB33Y80/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.