| Records |
| Author |
Escapil-Inchauspe, P.; Jerez-Hanckes, C. |
| Title |
Helmholtz Scattering by Random Domains: First-Order Sparse Boundary Elements Approximation |
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| Year |
2020 |
Publication |
SIAM Journal of Scientific Computing |
Abbreviated Journal |
SIAM J. Sci. Comput. |
| Volume |
42 |
Issue |
5 |
Pages |
A2561-A2592 |
| Keywords |
Helmholtz equation; shape calculus; uncertainty quantification; boundary element method; combination technique |
| Abstract |
We consider the numerical solution of time-harmonic acoustic scattering by obstacles with uncertain geometries for Dirichlet, Neumann, impedance, and transmission boundary conditions. In particular, we aim to quantify diffracted fields originated by small stochastic perturbations of a given relatively smooth nominal shape. Using first-order shape Taylor expansions, we derive tensor deterministic first-kind boundary integral equations for the statistical moments of the scattering problems considered. These are then approximated by sparse tensor Galerkin discretizations via the combination technique [M. Griebel, M. Schneider, and C. Zenger, A combination technique for the solution of sparse grid problems, in Iterative Methods in Linear Algebra, P. de Groen and P. Beauwens, eds., Elsevier, Amsterdam, 1992, pp. 263-281; H. Harbrecht, M. Peters, and M. Siebenmorgen, J. Comput. Phys., 252 (2013), pp. 128-141]. We supply extensive numerical experiments confirming the predicted error convergence rates with polylogarithmic growth in the number of degrees of freedom and accuracy in approximation of the moments. Moreover, we discuss implementation details such as preconditioning to finally point out further research avenues. |
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1064-8275 |
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UAI @ eduardo.moreno @ |
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1205 |
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| Author |
Escapil-Inchauspé, P.; Ruz, G.A. |
| Title |
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems |
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| Year |
2023 |
Publication |
Neurocomputing |
Abbreviated Journal |
Neurocomputing |
| Volume |
561 |
Issue |
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Pages |
126826 |
| Keywords |
Physics-informed neural networks; Hyper-parameter optimization; Bayesian optimization; Helmholtz equation |
| Abstract |
We consider physics-informed neural networks (PINNs) (Raissiet al., 2019) for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via Gaussian processes-based Bayesian optimization. We apply the HPO to Helmholtz equation for bounded domains and conduct a thorough study, focusing on: (i) performance, (ii) the collocation points density r and (iii) the frequency kappa, confirming the applicability and necessity of the method. Numerical experiments are performed in two and three dimensions, including comparison to finite element methods. |
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0925-2312 |
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WOS:001104342800001 |
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UAI @ alexi.delcanto @ |
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1912 |
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| Author |
Fierro, I.; Jerez-Hanckes, C. |
| Title |
Fast Calderon preconditioning for Helmholtz boundary integral equations |
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| Year |
2020 |
Publication |
Journal Of Computational Physics |
Abbreviated Journal |
J. Comput. Phys. |
| Volume |
409 |
Issue |
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Pages |
22 pp |
| Keywords |
Operator preconditioning; Calderon preconditioning; Helmholtz equations; Hierarchical matrices; Fast solvers; Boundary elements method |
| Abstract |
Calderon multiplicative preconditioners are an effective way to improve the condition number of first kind boundary integral equations yielding provable mesh independent bounds. However, when discretizing by local low-order basis functions as in standard Galerkin boundary element methods, their computational performance worsens as meshes are refined. This stems from the barycentric mesh refinement used to construct dual basis functions that guarantee the discrete stability of L-2-pairings. Based on coarser quadrature rules over dual cells and H-matrix compression, we propose a family of fast preconditioners that significantly reduce assembly and computation times when compared to standard versions of Calderon preconditioning for the three-dimensional Helmholtz weakly and hyper-singular boundary integral operators. Several numerical experiments validate our claims and point towards further enhancements. (C) 2020 Elsevier Inc. All rights reserved. |
| Address |
[Fierro, Ignacia] UCL, Dept Math, Gower St, London, England, Email: carlos.jerez@uai.cl |
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Academic Press Inc Elsevier Science |
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English |
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0021-9991 |
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WOS:000522726000020 |
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UAI @ eduardo.moreno @ |
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1153 |
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