Neural network approximation of numeric subsurface models in combinational optimization

PDF Version Also Available for Download.

Description

The ANN-GA approach to design optimization integrates two well-known computational technologies, artificial neural networks (AN%) and the genetic algorithm (GA), with a simple scheme for exploiting a network of common workstations to reduce the computational burden associated with applying formal optimization techniques to subsurface engineering problems. The greatest computational investment in a design project of the kind which will be described in this paper is in the simulation of physical processes needed to calculate the cost function. The ANN-GA methodology addresses this problem by training ANNs to stand in for the simulator during the course of a search directed by ... continued below

Physical Description

903 Kilobytes

Creation Information

Johnson, V M & Rogers, L L September 4, 1998.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

The ANN-GA approach to design optimization integrates two well-known computational technologies, artificial neural networks (AN%) and the genetic algorithm (GA), with a simple scheme for exploiting a network of common workstations to reduce the computational burden associated with applying formal optimization techniques to subsurface engineering problems. The greatest computational investment in a design project of the kind which will be described in this paper is in the simulation of physical processes needed to calculate the cost function. The ANN-GA methodology addresses this problem by training ANNs to stand in for the simulator during the course of a search directed by the GA. The ANNs are trained and tested from examples stored in a reusable knowledge base of representative simulations which relate variations in the design parameters to predicted outcomes for the particular engineering problem being studied. The maximum amount of information from each simulation is saved, subject to storage limitations. The creation of the knowledge base is itself a sizeable computational investment, one that pays off if it is used to train a variety of networks for different searches and/or for use in other contexts such as sensitivity analyses. A diagram of the components of the methodology is given in Figure 1. Applications of the methodology have been reported in

Physical Description

903 Kilobytes

Source

  • International Joint Conference on Information Science, Research Triangle Park, NC, October 23-28, 1998

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Other: DE00002589
  • Report No.: UCRL-JC-131828
  • Grant Number: W-7405-Eng-48
  • Office of Scientific & Technical Information Report Number: 2589
  • Archival Resource Key: ark:/67531/metadc671692

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • September 4, 1998

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

Description Last Updated

  • May 6, 2016, 10:56 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 6

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Johnson, V M & Rogers, L L. Neural network approximation of numeric subsurface models in combinational optimization, article, September 4, 1998; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc671692/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.