$p$-multilevel Monte Carlo for acoustic scattering from large deviation rough random surfaces
J\"urgen D\"olz, Wei Huang, and Michael Multerer

TL;DR
This paper introduces a $p$-multilevel Monte Carlo method to efficiently quantify uncertainties in acoustic scattering problems involving large, rough, and non-smooth random surface deviations, which are challenging for traditional methods.
Contribution
The paper demonstrates that $p$-multilevel Monte Carlo can effectively handle large, rough random deviations in acoustic scattering, filling a gap left by existing uncertainty quantification methods.
Findings
Efficient uncertainty quantification for large deviation rough surfaces.
Stable implementation of polynomial approximation via barycentric interpolation.
Numerical experiments confirm the method's effectiveness in 3D scattering geometry.
Abstract
We study time harmonic acoustic scattering on large deviation rough random scatterers. Therein, the roughness of the scatterers is caused by a low Sobolev regularity in the covariance function of their deformation field. The motivation for this study arises from physical phenomena where small-scale material defects can potentially introduce non-smooth deviations from a reference domain. The primary challenge in this scenario is that the scattered wave is also random, which makes computational predictions unreliable. Therefore, it is essential to quantify these uncertainties to ensure robust and well-informed design processes. While existing methods for uncertainty quantification typically rely on domain mapping or perturbation approaches, it turns out that large and rough random deviations are not satisfactory covered. To close this gap, and although counter intuitive at first, we show…
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Taxonomy
TopicsGeophysical Methods and Applications · Probabilistic and Robust Engineering Design · Electromagnetic Scattering and Analysis
