Tasmanian

"The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN is a collection of robust libraries for high dimensional integration and interpolation as well as parameter calibration. The code consists of several modules that can be used individually or conjointly.

Sparse Grids refers to a family of algorithms for approximation of multidimensional functions and integrals, where the approximation operator is constructed as a linear combination of tensors of multiple one dimensional operators. The TASMANIAN sparse grids library (henceforth called “TASMANIAN” or “the library”) implements a wide variety of sparse grids methods with different one dimensional operators and different ways of constructing the linear combination of tensors.

The components of the package are:

1) libtasmaniansparsegrids.a - the main component of TASMANIAN is the C++ library that implements the TasmanianSparseGrid class that encapsulates all the available capabilities

2) tasgrid - an executable that provides a command line interface to the library. The executable reads and writes data to text files and every command generally reads an instance of TasmanianSparseGrid class from a text file, calls a function from the class, and writes the modified class back to a text file.

3) Matlab interface - a series of MATLAB functions that call the executable tasgrid and read the result into MATLAB matrices. Note: the MATLAB interface does not use .mex files, thus the library can be compiled with a wider range of compilers than those supported by MATLAB, however, the usage of the interface is somewhat different than regular mex files.

4) Python Interface - a single Python module that implements a Python sparse grids class that mimics closely the behavior of the C++ library. The interface is based on ctypes, where a C++ instance of the TASMANIAN class is held by a void pointer, accessed via a C interface, and encapsulated by the Python module

http://tasmanian.ornl.gov/

http://tasmanian.ornl.gov/documents/UserManual.pdf

http://equinox.ornl.gov/research.html

https://github.com/csteed