A registration method for reduced basis problems using linear optimal transport

TL;DR

This paper introduces a data-driven registration method based on optimal transport theory to improve the efficiency of reduced basis methods for parametric PDEs with moving features and advection effects.

Contribution

It proposes a novel registration approach using Monge embeddings that enhances model reduction for PDEs with complex, moving solutions, incorporating a hyper-parameter for interpretability.

Findings

Improved approximation of parametric PDE solutions with moving features.

Demonstrated effectiveness on probability density and non-density solutions.

Provides a data-driven, interpretable registration method.

Abstract

We present a registration method for model reduction of parametric partial differential equations with dominating advection effects and moving features. Registration refers to the use of a parameter-dependent mapping to make the set of solutions to these equations more amicable for approximation using classical reduced basis methods. The proposed approach utilizes concepts from optimal transport theory, as we utilize Monge embeddings to construct these mappings in a purely data-driven way. The method relies on one interpretable hyper-parameter. We discuss how our approach relates to existing works that combine model order reduction and optimal transport theory. Numerical results are provided to demonstrate the effect of the registration. This includes a model problem where the solution is itself a probability density and one where it is not.