An Application of Gaussian Process Modeling for High-order Accurate Adaptive Mesh Refinement Prolongation
Abstract
We present a new polynomial-free prolongation scheme for Adaptive Mesh Refinement (AMR) simulations of compressible and incompressible computational fluid dynamics. The new method is constructed using a multi-dimensional kernel-based Gaussian Process (GP) prolongation model. The formulation for this scheme was inspired by the GP methods introduced by A. Reyes et al. (A New Class of High-Order Methods for Fluid Dynamics Simulation using Gaussian Process Modeling, Journal of Scientific Computing, 76 (2017), 443-480; A variable high-order shock-capturing finite difference method with GP-WENO, Journal of Computational Physics, 381 (2019), 189-217). In this paper, we extend the previous GP interpolations and reconstructions to a new GP-based AMR prolongation method that delivers a high-order accurate prolongation of data from coarse to fine grids on AMR grid hierarchies. In compressible flow simulations special care is necessary to handle shocks and discontinuities in a stable manner. To meet this, we utilize the shock handling strategy using the GP-based smoothness indicators developed in the previous GP work by A. Reyes et al. We demonstrate the efficacy of the GP-AMR method in a series of testsuite problems using the AMReX library, in which the GP-AMR method has been implemented.
Keywords
Cite
@article{arxiv.2003.08508,
title = {An Application of Gaussian Process Modeling for High-order Accurate Adaptive Mesh Refinement Prolongation},
author = {Steven I. Reeves and Dongwook Lee and Adam Reyes and Carlo Graziani and Petros Tzeferacos},
journal= {arXiv preprint arXiv:2003.08508},
year = {2022}
}