Multi-fidelity methods for uncertainty propagation in kinetic equations

TL;DR

This paper surveys multi-fidelity methods combining high- and low-fidelity models to efficiently perform uncertainty quantification in kinetic equations, focusing on control variates and stochastic collocation techniques.

Contribution

It provides a comprehensive overview of recent multi-fidelity strategies for kinetic equations, emphasizing their application to hydrodynamic limits and surrogate model selection.

Findings

Multi-fidelity methods can significantly accelerate uncertainty quantification.

Control variates and stochastic collocation are effective strategies.

Application-oriented analysis highlights the importance of surrogate model choice.

Abstract

The construction of efficient methods for uncertainty quantification in kinetic equations represents a challenge due to the high dimensionality of the models: often the computational costs involved become prohibitive. On the other hand, precisely because of the curse of dimensionality, the construction of simplified models capable of providing approximate solutions at a computationally reduced cost has always represented one of the main research strands in the field of kinetic equations. Approximations based on suitable closures of the moment equations or on simplified collisional models have been studied by many authors. In the context of uncertainty quantification, it is therefore natural to take advantage of such models in a multi-fidelity setting where the original kinetic equation represents the high-fidelity model, and the simplified models define the low-fidelity surrogate models. The scope of this article is to survey some recent results about multi-fidelity methods for kinetic equations that are able to accelerate the solution of the uncertainty quantification process by combining high-fidelity and low-fidelity model evaluations with particular attention to the case of compressible and incompressible hydrodynamic limits. We will focus essentially on two classes of strategies: multi-fidelity control variates methods and bi-fidelity stochastic collocation methods. The various approaches considered are analyzed in light of the different surrogate models used and the different numerical techniques adopted. Given the relevance of the specific choice of the surrogate model, an application-oriented approach has been chosen in the presentation.