Published January 15, 2018 | Version v1
Book Metadata-only

A stochastic convergence analysis of random number generators as applied to error propagation using Monte Carlo method and unscented transformation technique

Creators

  • 1. Instrumentation Department, VES Institute of Technology, Mumbai (India)
  • 2. VES Institute of Technology, Mumbai (India)
  • 3. Reactor Physics Design Division, Bhabha Atomic Research Centre, Mumbai (India)

Description

This paper compares the stochastic convergence of the Uniform Random number generators of two simulation software namely Matlab and Python and establishes the significance in choosing the right random number generator for error propagation studies. It further discusses about the application of Gaussian type of these random number generators to nonlinear cases of Error propagation using the Monte Carlo method and unscented transformation technique by means of a nonlinear transformation of one dimensional random variable of nuclear data

Part of:
Proceedings of the international conference on linear algebra and its applications; Fourth DAE-BRNS theme meeting on generation and use of covariance matrices in the applications of nuclear data

Additional details

Publishing Information

Publisher
Manipal Academy of Higher Education
Imprint Place
Manipal (India)
Imprint Title
Proceedings of the international conference on linear algebra and its applications; Fourth DAE-BRNS theme meeting on generation and use of covariance matrices in the applications of nuclear data
Imprint Pagination
144 p.
Journal Page Range
p. 47-48

Conference

Title
international conference on linear algebra and its applications; 4. DAE-BRNS theme meeting on generation and use of covariance matrices in the applications of nuclear data
Acronym
ICLAA-2017
Dates
9-15 Dec 2017
Place
Manipal (India)

INIS

Country of Publication
India
Country of Input or Organization
India
INIS RN
50081164
Subject category
S97: MATHEMATICAL METHODS AND COMPUTING;
Resource subtype / Literary indicator
Conference
Descriptors DEI
COMPARATIVE EVALUATIONS; COMPUTER CODES; CONVERGENCE; DATA COVARIANCES; MONTE CARLO METHOD; NONLINEAR PROBLEMS; NUCLEAR DATA COLLECTIONS; STOCHASTIC PROCESSES
Descriptors DEC
CALCULATION METHODS; EVALUATION

Optional Information