Efficient iterative arbitrary high order methods: an adaptive bridge between low and high order

TL;DR

This paper introduces a new adaptive high order iterative scheme that enhances efficiency and accuracy, enabling natural p-adaptivity and easy integration into existing methods, demonstrated through hyperbolic PDE applications.

Contribution

It presents a novel modification of high order iterative schemes that achieves accuracy matching, p-adaptivity, and easy implementation without sacrificing parallelization.

Findings

Improved computational efficiency in hyperbolic PDE simulations.

Effective p-adaptivity with local a posteriori limiters.

Robust performance on classical gas dynamics benchmarks.

Abstract

We propose a new paradigm for designing efficient p-adaptive arbitrary high order methods. We consider arbitrary high order iterative schemes that gain one order of accuracy at each iteration and we modify them in order to match the accuracy achieved in a specific iteration with the discretization accuracy of the same iteration. Apart from the computational advantage, the new modified methods allow to naturally perform p-adaptivity, stopping the iterations when appropriate conditions are met. Moreover, the modification is very easy to be included in an existing implementation of an arbitrary high order iterative scheme and it does not ruin the possibility of parallelization, if this was achievable by the original method. An application to the Arbitrary DERivative (ADER) method for hyperbolic Partial Differential Equations (PDEs) is presented here. We explain how such framework can be interpreted as an arbitrary high order iterative scheme, by recasting it as a Deferred Correction (DeC) method, and how to easily modify it to obtain a more efficient formulation, in which a local a posteriori limiter can be naturally integrated leading to p-adaptivity and structure preserving properties. Finally, the novel approach is extensively tested against classical benchmarks for compressible gas dynamics to show the robustness and the computational efficiency.