Name
Title | ||
|---|---|---|
A New Class of High-Order Methods for Fluid Dynamics Simulations Using Gaussian Process Modeling: One-Dimensional Case. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10915-017-0625-2 | J. Sci. Comput. |
Keywords | Field | DocType |
Gaussian processes, Stochastic models, High-order methods, Finite volume method, Gas dynamics, Magnetohydrodynamics | Kernel (linear algebra),Mathematical optimization,Polynomial,Interpolation,Fluid dynamics,Gaussian process,Stochastic modelling,Computational fluid dynamics,Finite volume method,Mathematics | Journal |
Volume | Issue | ISSN |
76 | 1 | 0885-7474 |
Citations | PageRank | References |
1 | 0.35 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam Reyes | 1 | 4 | 1.06 |
| Dongwook Lee | 2 | 418 | 62.32 |
| Carlo Graziani | 3 | 2 | 0.70 |
| petros tzeferacos | 4 | 39 | 4.74 |