python-randomfields

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python-randomfields

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A Python module that implements tools for the simulation and identification of random fields using the Karhunen-Loeve expansion representation.

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python-randomfields
===================

A Python module that implements tools for the simulation and identification of
random fields using the Karhunen-Loeve expansion representation.

Folder description
------------------

This folder contains:

* a Python module named randomfields,

* 4 Python scripts implementing basic examples, showing the ways the module
  functionalities can be used.
  NB: *_simulation_* scripts must be run before their corresponding
  *_identification_* counterpart because the simulation scripts generate
  input randomfield data (dumped to ascii file) for identification.

Requirements
------------

The present Python module and its examples rely on:

* OpenTURNS (>= 1.4)

* Numpy (>= 1.6)

* Scipy (>= 0.9)

* Matplotlib (>= 1.0)

Installation
------------

The example scripts can be run from this folder for testing. They'll import the
randomfields module from the local folder.

In order to make the randomfields module installation systemwide, you may
either:

* copy the randomfields module (directory) in the "site-package" directory
  of your Python distribution (e.g. /usr/local/lib/python2.7/site-package).
  NB: You might need admin rights to do so.

* append the parent directory of the randomfields module (directory) to
  your PYTHONPATH environment variable.

Documentation
-------------

The randomfields module uses Python docstrings. Use either "help(object)" in a
classic Python shell or "object?" in an improved Python (IPython) shell.

Authors and terms of use
------------------------

This module was implemented by Phimeca Engineering SA, EdF and Institut Navier
(ENPC). It is shipped as is without any warranty of any kind.

Todo
-----

Contributions are welcome.

* Implement other Galerkin schemes such as the Haar wavelet Galerkin scheme
  proposed by Phoon et al. (2002). More advanced (smoother) wavelets could
  also be used.

* Call for data: if you have any, please contribute, possibly along with
  an identification example.

* Any other idea within the scope of the module is welcome!

References
-----------

* Phoon, K.; Huang, S. & Quek, S.
  [Implementation of Karhunen-Loeve expansion for simulation using a
  wavelet-Galerkin scheme](
  http://www.eng.nus.edu.sg/civil/people/cvepkk/JPaper_2002_vol17.pdf)
  Prob. Eng. Mech., 2002, 17, 293-303

* Ghanem, R. & Spanos, P.
  Stochastic Finite Elements: A Spectral Approach (Revised edition)
  Dover Publications Inc., 2003, 224

* Sudret, B. & Der Kiureghian, A.
  [Stochastic Finite Element Methods and Reliability, A State-of-the-Art
  report](http://www.ibk.ethz.ch/su/publications/Reports/SFE-report-Sudret.pdf>)
  University of California, Berkeley, 2000

* Desceliers, C.; Soize, C. & Ghanem, R.
  [Identification of chaos representations of elastic properties of random media
  using experimental vibration tests](
  http://hal.archives-ouvertes.fr/docs/00/68/61/50/PDF/publi-2007-CM-39_6_831-838-desceliers-soize-ghanem-preprint.pdf)
  Comput. Mech., 2007, 39, 831-838

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