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NISP

(1058/7558 downloads)
Non Intrusive Spectral Projection
Details
Version
2.6-1
Author(s)
Jean-Marc Martinez
Michael Baudin
John Burkardt
Knut Petras
Chong Gu
Entity
- -
Package maintainer
Michael Baudin
License
Supported Scilab Versions
>= 5.4
Dependencies
Stixbox (≥ 2.2)
Distfun (≥ 0.7)
Apifun (≥ 0.2)
Specfun (≥ 0.4)
Creation Date
10th of April 2013
ATOMS packaging system
Available on
How To Install
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. 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 (2008-2011) 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. Features -------- Main Features: * randvar: * Manage various types of random variables * uniform, normal, exponential, log-normal * setrandvar: * Manage various sampling methods for sets of random variables * Monte-Carlo, Sobol Quasi-Random, Latin Hypercube Sampling, LHS Max Min sampling, and various samplings based on Smolyak Cubature points. * polychaos: * Manage polynomial chaos expansion and get specific outputs * mean, variance, sensitivity indices, quantiles, Wilks quantiles, correlation, etc... * Generate a stand-alone C source code which computes the output of the polynomial chaos expansion. Tutorials * nisp_theory : Introduction to PC decomposition. * setrandvar_tutorial : A tutorial for the designs from setrandvar. Configuration Functions: * nisp_destroyall : Destroy all current objects. * nisp_getpath : Returns the path to the current module. * nisp_initseed : Sets the seed of the uniform random number generator. * nisp_printall : Prints all current objects. * nisp_shutdown : Shuts down the NISP toolbox. * nisp_startup : Starts up the NISP toolbox. * nisp_verboselevelget : Returns the current verbose level. * nisp_verboselevelset : Sets the current verbose level. Sensitivity Analysis * nisp_bruteforcesa : Compute sensitivity indices by brute force. * nisp_sobolsaAll : Compute sensitivity indices by Sobol, Ishigami, Homma. * nisp_sobolsaFirst : Compute sensitivity indices by Sobol, Ishigami, Homma. * nisp_sobolsaTotal : Compute sensitivity indices by Sobol, Ishigami, Homma. Test functions: * nisp_ishigami : Returns the Ishigami function. * nisp_ishigamisa : Exact sensitivity analysis for the Ishigami function * nisp_product : Returns the value of the Product function * nisp_productsa : Exact sensitivity analysis for the Product function * nisp_sum : Returns the value of the Product function * nisp_sumsa : Returns the sensitivity indices of the Sum function Acknowledgements ---------------- * Paul Beaucaire * Allan Cornet
Files (6)
[1.15 Mo] NISP_2.6-1.bin.x64.windows.zip
Windows version (x64)
Automatically generated by the ATOMS compilation chain

[1.16 Mo] NISP_2.6-1.bin.windows.zip
Windows version (i686)
Automatically generated by the ATOMS compilation chain

[960.65 Ko] NISP_2.6-1.bin.x86_64.darwin.tar.gz
MacOSX version
Automatically generated by the ATOMS compilation chain

[1003.58 Ko] NISP_2.6-1.bin.x86_64.linux.tar.gz
Linux version (x86_64)
Automatically generated by the ATOMS compilation chain

[1021.53 Ko] NISP_2.6-1.bin.i686.linux.tar.gz
Linux version (i686)
Automatically generated by the ATOMS compilation chain

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Comments (1)
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Comment from Michael Baudin -- 18th of April 2013, 10:55:26 AM    
Here is the changelog of this release.

NISP (2.6, April 2013)
 * Fixed bug #1008
   The sampling function of nisp_bruteforcesa was 
   too complicated.
   There is no step argument anymore in the calling 
   sequence of randgen.
 * Fixed bug #1021: 
   polychaos_getinvquantile had wrong name and help.
   Simplified polychaos_buildsample for simplicity:
   the "order" argument is not required anymore, 
   which avoids errors from the user.
   The sample is now always ordered.
 * Fixed bug #1041: 
   The help for maximin LHS design was unclear.
   Added a tutorial for the various designs 
   from the setrandvar class.
 * Fixed bug #1045: 
   There are impossible calls to polychaos_computeexp. 
   In the help page, added the map of possible combinations between the 
   computation of the coefficients (integration or regression) 
   and the sampling (e.g. Monte-Carlo or Quadrature).
   Clarified the error messages for integration and regression.
 * Removed exponential, lognormal and erfcinv functions.
   This is because these functions are available in the "distfun" module.
   To update your code, apply the following replacements:
   
   nisp_erfcinv > distfun_erfcinv
   nisp_exppdf > distfun_exppdf
   nisp_expcdf > distfun_expcdf
   nisp_expinv > distfun_expinv
   nisp_lognormalpdf > distfun_lognormalpdf
   nisp_lognormalcdf > distfun_lognormalcdf
   nisp_lognormalinv > distfun_lognormalinv
   
   This fixes the bug #1017: 
   Exponential and LogNormal distributions were duplicated.
   This fixes the bug #1016: 
   nisp_erfcinv was duplicated in distfun.
 * Removed the obsolete nisp_sobolsa function.
   Use the nisp_sobolFirst function instead.
 * Removed the nisp_cov function.
   Use Stixbox's cov function instead.
   This fixes bug #1049
   http://forge.scilab.org/index.php/p/nisp/issues/1049/
   To update your code, please replace:
   
c=nisp_cov(x,y)

with

c=cov(x,y)

 * Removed nisp_corrcoef function.
   Use Stixbox's corrcoef function instead.
   To update your code, please replace:
 
r = nisp_corrcoef ( x , y )

with

r = corrcoef ( x , y )

 * Removed nisp_buildlhs function.
   Use scidoe module instead.
   This fixes bug #1054.
   http://forge.scilab.org/index.php/p/nisp/issues/1054/
   To update your code, please replace:
 
sampling=nisp_buildlhs(s,n)

with

sampling=scidoe_lhsdesign(s,n)
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