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Adaptively Learning an Importance Function Using Transport Constrained Monte Carlo

  • Thomas E. Booth
Conference paper

Abstract

It is well known that a Monte Carlo estimate can be obtained with zero-variance if an exact importance function for the estimate is known. There are many ways that one might iteratively seek to obtain an ever more exact importance function. This paper describes a method that has obtained ever more exact importance functions that empirically produce an error that is dropping exponentially with computer time. The method described herein constrains the importance function to satisfy the (adjoint) Boltzmann transport equation. This constraint is provided by using the known form of the solution, usually referred to as the Case eigenfunction solution.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Thomas E. Booth
    • 1
  1. 1.Code Integration Group XCILos Alamos National LaboratoryLos AlamosUSA

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