Large deformation shape uncertainty quantification in acoustic scattering

We address shape uncertainty quantification for the two-dimensional Helmholtz transmission problem, where the shape of the scatterer is the only source of uncertainty. In the framework of the so-called deterministic approach, we provide a high-dimensional parametrization for the interface. Each domain configuration is mapped to a nominal configuration, obtaining a problem on a fixed domain with stochastic coefficients. To compute surrogate models and statistics of quantities of interest, we apply an adaptive, anisotropic Smolyak algorithm, which allows to attain high convergence rates that are independent of the number of dimensions activated in the parameter space. We also develop a regularity theory with respect to the spatial variable, with norm bounds that are independent of the parametric dimension. The techniques and theory presented in this paper can be easily generalized to any elliptic problem on a stochastic domain.

[1]  C. Schwab,et al.  Electromagnetic wave scattering by random surfaces: Shape holomorphy , 2017 .

[2]  Helmut Harbrecht,et al.  First order second moment analysis for stochastic interface problems based on low-rank approximation , 2013 .

[3]  Fabio Nobile,et al.  A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data , 2008, SIAM J. Numer. Anal..

[4]  Reinhold Schneider,et al.  Sparse second moment analysis for elliptic problems in stochastic domains , 2008, Numerische Mathematik.

[5]  R. DeVore,et al.  Analytic regularity and polynomial approximation of parametric and stochastic elliptic PDEs , 2010 .

[6]  Nicolas Moës,et al.  An extended stochastic finite element method for solving stochastic partial differential equations on random domains , 2008 .

[7]  Claudio Canuto,et al.  A fictitious domain approach to the numerical solution of PDEs in stochastic domains , 2007, Numerische Mathematik.

[8]  Helmut Harbrecht,et al.  Analysis of the domain mapping method for elliptic diffusion problems on random domains , 2016, Numerische Mathematik.

[9]  R. Tempone,et al.  Stochastic Spectral Galerkin and Collocation Methods for PDEs with Random Coefficients: A Numerical Comparison , 2011 .

[10]  N. Levenberg,et al.  Function theory in several complex variables , 2001 .

[11]  Thomas Gerstner,et al.  Dimension–Adaptive Tensor–Product Quadrature , 2003, Computing.

[12]  W. McLean Strongly Elliptic Systems and Boundary Integral Equations , 2000 .

[13]  L. Grafakos Classical and modern Fourier analysis , 2003 .

[14]  F. Nobile,et al.  Analytic regularity and collocation approximation for PDEs with random domain deformations , 2014 .

[15]  J. Nédélec Acoustic and Electromagnetic Equations : Integral Representations for Harmonic Problems , 2001 .

[16]  Helmut Harbrecht,et al.  Computing quantities of interest for random domains with second order shape sensitivity analysis , 2015 .

[17]  Michael Griebel,et al.  On tensor product approximation of analytic functions , 2016, J. Approx. Theory.

[18]  R. Kress,et al.  Inverse Acoustic and Electromagnetic Scattering Theory , 1992 .

[19]  Moulay Abdellah Chkifa On the Lebesgue constant of Leja sequences for the complex unit disk and of their real projection , 2013, J. Approx. Theory.

[20]  Albert Cohen,et al.  Breaking the curse of dimensionality in sparse polynomial approximation of parametric PDEs , 2015 .

[21]  Robert N. Gantner,et al.  A Generic C++ Library for Multilevel Quasi-Monte Carlo , 2016, PASC.

[22]  P. Bassanini,et al.  Elliptic Partial Differential Equations of Second Order , 1997 .

[23]  Jean-Pierre Berenger,et al.  A perfectly matched layer for the absorption of electromagnetic waves , 1994 .

[24]  Jens Markus Melenk,et al.  Optimal a priori estimates for higher order finite elements for elliptic interface problems , 2010 .

[25]  Daniel M. Tartakovsky,et al.  Stochastic analysis of transport in tubes with rough walls , 2006, J. Comput. Phys..

[26]  Dongbin Xiu,et al.  High-Order Collocation Methods for Differential Equations with Random Inputs , 2005, SIAM J. Sci. Comput..

[27]  Pingwen Zhang,et al.  Moving mesh methods in multiple dimensions based on harmonic maps , 2001 .

[28]  Christoph Schwab,et al.  Computational Higher Order Quasi-Monte Carlo Integration , 2014, MCQMC.

[29]  S. Osher,et al.  Level set methods: an overview and some recent results , 2001 .

[30]  Helmut Harbrecht Mathematisches Numerical Solution of Elliptic Diffusion Problems on Random Domains , 2015 .

[31]  Laura Scarabosio,et al.  Shape uncertainty quantification for scattering transmission problems , 2016 .

[32]  C. Schwab,et al.  Sparsity in Bayesian inversion of parametric operator equations , 2014 .

[33]  Josef Dick,et al.  Multilevel higher-order quasi-Monte Carlo Bayesian estimation , 2016, 1611.08324.

[34]  Albert Cohen,et al.  Convergence Rates of Best N-term Galerkin Approximations for a Class of Elliptic sPDEs , 2010, Found. Comput. Math..

[35]  R. DeVore,et al.  ANALYTIC REGULARITY AND POLYNOMIAL APPROXIMATION OF PARAMETRIC AND STOCHASTIC ELLIPTIC PDE'S , 2011 .

[36]  Matti Lassas,et al.  On the existence and convergence of the solution of PML equations , 1998, Computing.

[37]  Albert Cohen,et al.  High-Dimensional Adaptive Sparse Polynomial Interpolation and Applications to Parametric PDEs , 2013, Foundations of Computational Mathematics.

[38]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

[39]  Daniel M. Tartakovsky,et al.  Numerical Methods for Differential Equations in Random Domains , 2006, SIAM J. Sci. Comput..

[40]  N. Cutland,et al.  On homogeneous chaos , 1991, Mathematical Proceedings of the Cambridge Philosophical Society.

[41]  Endre Süli,et al.  The Adaptive Computation of Far-Field Patterns by A Posteriori Error Estimation of Linear Functionals , 1998 .

[42]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[43]  L. R. Scott,et al.  The Mathematical Theory of Finite Element Methods , 1994 .

[44]  Claude Jeffrey Gittelson,et al.  Sparse tensor discretizations of high-dimensional parametric and stochastic PDEs* , 2011, Acta Numerica.

[45]  Claude Jeffrey Gittelson,et al.  Adaptive stochastic Galerkin FEM , 2014 .

[46]  Fabio Nobile,et al.  A stochastic collocation method for the second order wave equation with a discontinuous random speed , 2013, Numerische Mathematik.

[47]  Fabio Nobile,et al.  Analytic regularity and collocation approximation for elliptic PDEs with random domain deformations , 2013, Comput. Math. Appl..

[48]  Peter Monk,et al.  The Perfectly Matched Layer in Curvilinear Coordinates , 1998, SIAM J. Sci. Comput..

[49]  R. Hiptmair,et al.  Trefftz Approximations: A New Framework for Nonreflecting Boundary Conditions , 2016, IEEE Transactions on Magnetics.

[50]  C. Schwab,et al.  Sparsity in Bayesian inversion of parametric operator equations , 2013 .

[51]  G. Allaire,et al.  A level-set method for shape optimization , 2002 .

[52]  Jean-Paul Calvi,et al.  Lagrange interpolation at real projections of Leja sequences for the unit disk , 2011 .

[53]  Christoph Schwab,et al.  Sparse Tensor Discretization of Elliptic sPDEs , 2009, SIAM J. Sci. Comput..

[54]  Fabio Nobile,et al.  A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data , 2007, SIAM Rev..

[55]  Christoph Schwab,et al.  Sparse, adaptive Smolyak quadratures for Bayesian inverse problems , 2013 .