# Simplex Stochastic Collocation for Piecewise Smooth Functions with Kinks

## TL;DR

This paper introduces a novel stochastic collocation method that efficiently approximates piecewise smooth functions with kinks by leveraging known smooth regions, achieving improved convergence in high-dimensional uncertainty quantification tasks.

## Contribution

It proposes a method that approximates each smooth region separately, using only region indicators, to handle non-smooth functions with kinks in high dimensions.

## Key findings

- Achieves a global convergence order of (p+1)/d.
- Effectively handles functions with kinks without knowing exact kink locations.
- Applicable to uncertainty quantification in gas network models.

## Abstract

Most approximation methods in high dimensions exploit smoothness of the function being approximated. These methods provide poor convergence results for non-smooth functions with kinks. For example, such kinks can arise in the uncertainty quantification of quantities of interest for gas networks. This is due to the regulation of the gas flow, pressure, or temperature. But, one can exploit that for each sample in the parameter space it is known if a regulator was active or not, which can be obtained from the result of the corresponding numerical solution. This information can be exploited in a stochastic collocation method. We approximate the function separately on each smooth region by polynomial interpolation and obtain an approximation to the kink. Note that we do not need information about the exact location of kinks, but only an indicator assigning each sample point to its smooth region. We obtain a global order of convergence of $(p+1)/d$, where $p$ is the degree of the employed polynomials and $d$ the dimension of the parameter space.