A unified framework for mesh refinement in random and physical space

Abstract

In recent work we have shown how an accurate reduced model can be utilized to perform mesh refinement in random space. That work relied on the explicit knowledge of an accurate reduced model which is used to monitor the transfer of activity from the large to the small scales of the solution. Since this is not always available, we present in the current work a framework which shares the merits and basic idea of the previous approach but does not require an explicit knowledge of a reduced model. Moreover, the current framework can be applied for refinement in both random and physical space. In this manuscript we focus on the application to random space mesh refinement. We study examples of increasing difficulty (from ordinary to partial differential equations) which demonstrate the efficiency and versatility of our approach. We also provide some results from the application of the new framework to physical space mesh refinement.

29 pages

References in corpus (1)

• Mesh refinement for uncertainty quantification through model reduction

Cited by in corpus (4)

• A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness
• When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
• Model reduction for a power grid model
• Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems