Journal
JOURNAL OF SCIENTIFIC COMPUTING
Volume 76, Issue 1, Pages 443-480Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10915-017-0625-2
Keywords
Gaussian processes; Stochastic models; High-order methods; Finite volume method; Gas dynamics; Magnetohydrodynamics
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Funding
- National Science Foundation [AST-0909132]
- U.S. DOE NNSA ASC through the Argonne Institute for Computing in Science [57789]
- U.S. DOE NNSA-ASC
- OS-OASCR
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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.
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