The full poster is here.
Sources are available online as part of the open-source package FMR. They can be downloaded for free at the following address https://gforge.inria.fr/projects/fmr Dependencies FMR relies on
ScalFMM  for performing fast multipole matrix multiplication in parallel (in shared and distributed memory)
MKL for dense linear algebra and FFT
Scotch or CClusteringLib for partitionning Features The package provides:
routines for generating Gaussian Random Fields based on
standard LRA: Cholesky Decomposition, SVD or FFT for regular grids.
- randomized LRA: RandSVD and Nystrom method with uniform or leverage score-based sampling.
- a variety of correlation kernels: Mat´ern, Spherical model, Oseen-Gauss.
- a Python interface for MDS using Randomized SVD or Nystrom
- a Matlab interface for Ensemble Kalman Filtering
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