A New Class of High-Order Methods for Fluid Dynamics Simulations Using Gaussian Process Modeling: One-Dimensional Case

被引:7
|
作者
Reyes, Adam [1 ]
Lee, Dongwook [2 ]
Graziani, Carlo [3 ]
Tzeferacos, Petros [3 ,4 ]
机构
[1] Univ Calif Santa Cruz, Dept Phys, Santa Cruz, CA 95064 USA
[2] Univ Calif Santa Cruz, Appl Math & Stat, Santa Cruz, CA 95064 USA
[3] Univ Chicago, Dept Astron & Astrophys, Flash Ctr Computat Sci, Chicago, IL 60637 USA
[4] Univ Oxford, Dept Phys, Oxford, England
基金
美国国家科学基金会;
关键词
Gaussian processes; Stochastic models; High-order methods; Finite volume method; Gas dynamics; Magnetohydrodynamics; FINITE-VOLUME METHOD; RADIAL BASIS FUNCTIONS; EFFICIENT IMPLEMENTATION; POSTSHOCK OSCILLATIONS; CONSERVATION-LAWS; RIEMANN PROBLEMS; EQUATIONS; SCHEMES; INTERPOLATION; ACCURATE;
D O I
10.1007/s10915-017-0625-2
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We introduce an entirely new class of high-order methods for computational fluid dynamics based on the Gaussian process (GP) family of stochastic functions. Our approach is to use kernel-based GP prediction methods to interpolate/reconstruct high-order approximations for solving hyperbolic PDEs. We present a new high-order formulation to solve (magneto)hydrodynamic equations using the GP approach that furnishes an alternative to conventional polynomial-based approaches.
引用
收藏
页码:443 / 480
页数:38
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