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SDE BASED REGRESSION FOR LINEAR RANDOM PDEs
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DOI：10.1137/16M1060637.png)
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A simulation based method for the numerical solution of PDEs with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation (SDE) driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behavior.
Keyword:
partial differential equations with random coefficients
random PDE
uncertainty quantification
Feynman-Kac
stochastic differential equations
stochastic simulation
stochastic regression
Monte Carlo
Euler Maruyama
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