Sensitivity analysis of burgers' equation with shocks
The generalized polynomial chaos (gPC) method has been extensively used in uncertainty quantification problems where equations contain random variables. For gPC to achieve high accuracy, PDE solutions need to have high regularity in the random space, but this is what hyperbolic type problems cannot provide. We provide a counterargument in this paper and show that even though the solution profile develops singularities in the random space, which destroys the spectral accuracy of gPC, the physical quantities (such as the shock emergence time, the shock location, and the shock strength) are all smooth functions of the uncertainties coming from both initial data and the wave speed. With proper shifting, the solution's polynomial interpolation approximates the real solution accurately, and the error decays as the order of the polynomial increases. Therefore this work provides a new perspective to “quantify uncertainties” and significantly improves the accuracy of the gPC method with a slight reformulation. We use the Burgers' equation as an example for thorough analysis, and the analysis could be extended to general conservation laws with convex fluxes.
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- 4905 Statistics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics
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Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- 4905 Statistics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics