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NISP

Non Intrusive Spectral Projection
(3073 downloads for this version - 34644 downloads for all versions)
Details
Version
2.2
A more recent valid version with binaries for Scilab 5.3 exists: 2.4
Authors
Jean-Marc Martinez
Michael Baudin
Owner Organization
CEA / INRIA
Maintainers
Allan Cornet
Michael Baudin
License
Dependencies
Creation Date
January 31, 2011
Source created on
Scilab 5.3.x
Binaries available on
Scilab 5.3.x:
Linux 32-bit Windows 64-bit Linux 64-bit Windows 32-bit MacOSX
Install command
--> atomsInstall("NISP")
Description
            This module allows to perform sensitivity analysis. 
This is the analysis of the uncertainty in the 
output of a given model, depending on the uncertainty in its 
inputs. 

The analysis is based on chaos polynomials, which are orthogonal polynomials
which are used as an approximation of the original model. Once the coefficients
of the chaos polynomial are computed, the associated sensitivity indices are
straightforward to get.

The module provides the following components :
 * “nisp” provides function to configure the global behaviour of the 
   toolbox. This allows to startup and shutdown the library, configure and
   quiery the verbose level or initialize the seed of the random number
   generator.
 * “randvar” is the class which allows to manage a random variable. 
   Various types of random variables are available, including uniform,
   normal, exponential, etc...
 * “setrandvar”, is the class which allows to manage a set of random 
   variables. Several methods are available to build a sampling from a set
   of random variables. We can use, for example, a Monte-Carlo sampling or
   a Sobol low discrepancy sequence. This feature allows to use the class
   as Design of Experiment tool (DOE).
 * “polychaos” is the class which allows to manage a polynomial chaos 
   expansion. The coefficients of the expansion are computed based on given
   numerical experiments which creates the association between the inputs
   and the outputs. Once computed, the expansion can be used as a regular
   function. The mean, standard deviation or quantile can also be directly
   retrieved.
The current toolbox provides an object-oriented approach of the C++ NISP
library.

The following list presents the features provided by the NISP toolbox :
 * Manage various types of random variables : uniform, normal, exponential,
log-normal,
 * Generate random numbers from a given random variable,
 * Transform an outcome from a given random variable into another,
 * Manage various sampling methods for sets of random variables: Monte-Carlo,
Sobol, Latin Hypercube Sampling, various samplings based on Smolyak.
 * Manage polynomial chaos expansion and get specific outputs, including: mean,
variance, quantile, correlation, etc...
Generate the C source code which computes the output of the polynomial chaos
expansion.

This toolbox has been created in the context of the 
OPUS project :

http://opus-project.fr/

within the workpackage 2.1.1 "Construction de méta-modèles".
This project has received funding by Agence Nationale de la recherche :

http://www.agence-nationale-recherche.fr/

See in the help provided in the help/en_US directory of the 
toolbox for more information about its use.
Use cases are presented in the demos directory.            
Files (7)
[3.39 kB]
Miscellaneous file
This is the changelog of the NISP module.
[6.15 MB]
Source code archive
Disabled (pragma) the optimization for the Sobol sequence: fails to compile on
some Windows.
[6.40 MB]
Linux 32-bit binary for Scilab 5.3.x
Linux 32-bit
Automatically generated by the ATOMS compilation chain

[13.79 MB]
Windows 64-bit binary for Scilab 5.3.x
Windows 64-bit
Automatically generated by the ATOMS compilation chain

[6.40 MB]
Linux 64-bit binary for Scilab 5.3.x
Linux 64-bit
Automatically generated by the ATOMS compilation chain

[13.39 MB]
Windows 32-bit binary for Scilab 5.3.x
Windows 32-bit
Automatically generated by the ATOMS compilation chain

[6.35 MB]
MacOSX binary for Scilab 5.3.x
MacOSX version
Automatically generated by the ATOMS compilation chain

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