Dear Julia friends
It is my pleasure to present GaussianRandomFields, a package for generating Gaussian random fields in arbitrary dimension. These random fields can for example be used as input data for PDEs with random coefficients. A simulation of such a PDE typically requires many samples from the random field, that must be computed efficiently. Here’s an excerpt from the documentation, listing some key features:
- Support for stationary (isotropic and anisotropic) and separable non-stationary covariance functions.
- Default implementation of most standard covariance functions such as Gaussian, Exponential and Matérn covariances. Adding a user-defined covariance function is very easy.
- Implementation of most common methods to generate Gaussian random fields: Cholesky factorization, Karhunen-Loève expansion and circulant embedding.
- Easy generation of Gaussian random fields defined on a Finite Element mesh.
- Versatile plotting features for easy visualisation of Gaussian random fields.
Many examples and some nice pictures can found in the tutorial. This is a small teaser:
Any comments or remarks are highly appreciated!