scholarly journals A reduced basis method for parabolic partial differential equations with parameter functions and application to option pricing

2016 ◽  
Author(s):  
Antonia Mayerhofer ◽  
Karsten Urban
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Option Pricing ◽  
Reduced Basis Method ◽  
Parabolic Partial Differential Equations ◽  
Reduced Basis ◽  
Partial Differential ◽  
Basis Method

Reduced Basis Method Applied to Eigenvalue Problems from Convection

2019 ◽  
Vol 29 (03) ◽  
pp. 1950028
Author(s):  
Francisco Pla ◽  
Henar Herrero
Keyword(s):  
Stability Analysis ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Eigenvalue Problems ◽  
Stationary Solutions ◽  
Reduced Basis Method ◽  
Bifurcation Parameter ◽  
Reduced Basis ◽  
Partial Differential ◽  
Basis Method

The reduced basis method is a suitable technique for finding numerical solutions to partial differential equations that must be obtained for many values of parameters. This method is suitable when researching bifurcations and instabilities of stationary solutions for partial differential equations. It is necessary to solve numerically the partial differential equations along with the corresponding eigenvalue problems of the linear stability analysis of stationary solutions for a large number of bifurcation parameter values. In this paper, the reduced basis method has been used to solve eigenvalue problems derived from the linear stability analysis of stationary solutions in a two-dimensional Rayleigh–Bénard convection problem. The bifurcation parameter is the Rayleigh number, which measures buoyancy. The reduced basis considered belongs to the eigenfunction spaces derived from the eigenvalue problems for different types of solutions in the bifurcation diagram depending on the Rayleigh number. The eigenvalue with the largest real part and its corresponding eigenfunction are easily calculated and the bifurcation points are correctly captured. The resulting matrices are small, which enables a drastic reduction in the computational cost of solving the eigenvalue problems.

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