Proc Math Phys Eng Sci. 2017 Apr;473(2200):20160809. doi: 10.1098/rspa.2016.0809. Epub 2017 Apr 26.

Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models.

Proceedings. Mathematical, physical, and engineering sciences

A A Shah, W W Xing, V Triantafyllidis

PMID: 28484327 PMCID: PMC5415687 DOI: 10.1098/rspa.2016.0809

Abstract

In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach.

Keywords: Gaussian process model; manifold learning; nonlinear systems; parameter-dependent partial differential equations; proper orthogonal decomposition

Conflict of interest statement

Publication Types

• Journal Article