  A novel approach for non-intrusive uncertainty propagation is proposed. Our
approach overcomes the limitation of many traditional methods, such as
generalised polynomial chaos methods, which may lack sufficient accuracy when
the quantity of interest depends discontinuously on the input parameters. As a
remedy we propose an adaptive sampling algorithm based on minimum spanning
trees combined with a domain decomposition method based on support vector
machines. The minimum spanning tree determines new sample locations based on
both the probability density of the input parameters and the gradient in the
quantity of interest. The support vector machine efficiently decomposes the
random space in multiple elements, avoiding the appearance of Gibbs phenomena
near discontinuities. On each element, local approximations are constructed by
means of least orthogonal interpolation, in order to produce stable
interpolation on the unstructured sample set. The resulting minimum spanning
tree multi-element method does not require initial knowledge of the behaviour
of the quantity of interest and automatically detects whether discontinuities
are present. We present several numerical examples that demonstrate accuracy,
efficiency and generality of the method.
