A New Class of High-Order Methods for Fluid Dynamics Simulations Using Gaussian Process Modeling: One-Dimensional Case
| Autor: | Dongwook Lee, Adam Reyes, Petros Tzeferacos, Carlo Graziani |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Polynomial MathematicsofComputing_NUMERICALANALYSIS 010103 numerical & computational mathematics Computational fluid dynamics 01 natural sciences Theoretical Computer Science symbols.namesake Prediction methods Fluid dynamics Applied mathematics 0101 mathematics High order Gaussian process Magneto Mathematics Numerical Analysis business.industry Applied Mathematics General Engineering 010101 applied mathematics Computational Mathematics Computational Theory and Mathematics Kernel (statistics) symbols business Software |
| Zdroj: | Journal of Scientific Computing. 76:443-480 |
| ISSN: | 1573-7691 0885-7474 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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