LEADER 07285nam 22006735 450 001 9910751387703321 005 20251008163438.0 010 $a9783031383847 024 7 $a10.1007/978-3-031-38384-7 035 $a(MiAaPQ)EBC30786203 035 $a(CKB)28495827900041 035 $a(Au-PeEL)EBL30786203 035 $a(DE-He213)978-3-031-38384-7 035 $a(PPN)272913480 035 $a(EXLCZ)9928495827900041 100 $a20231013d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs /$fby Dinh D?ng, Van Kien Nguyen, Christoph Schwab, Jakob Zech 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (216 pages) 225 1 $aLecture Notes in Mathematics,$x1617-9692 ;$v2334 311 08$a9783031383830 327 $aIntro -- Preface -- Acknowledgement -- Contents -- List of Symbols -- List of Abbreviations -- 1 Introduction -- 1.1 An Example -- 1.2 Contributions -- 1.3 Scope of Results -- 1.4 Structure and Content of This Text -- 1.5 Notation and Conventions -- 2 Preliminaries -- 2.1 Finite Dimensional Gaussian Measures -- 2.1.1 Univariate Gaussian Measures -- 2.1.2 Multivariate Gaussian Measures -- 2.1.3 Hermite Polynomials -- 2.2 Gaussian Measures on Separable Locally Convex Spaces -- 2.2.1 Cylindrical Sets -- 2.2.2 Definition and Basic Properties of Gaussian Measures -- 2.3 Cameron-Martin Space -- 2.4 Gaussian Product Measures -- 2.5 Gaussian Series -- 2.5.1 Some Abstract Results -- 2.5.2 Karhunen-Loève Expansion -- 2.5.3 Multiresolution Representations of GRFs -- 2.5.4 Periodic Continuation of a Stationary GRF -- 2.5.5 Sampling Stationary GRFs -- 2.6 Finite Element Discretization -- 2.6.1 Function Spaces -- 2.6.2 Finite Element Interpolation -- 3 Elliptic Divergence-Form PDEs with Log-Gaussian Coefficient -- 3.1 Statement of the Problem and Well-Posedness -- 3.2 Lipschitz Continuous Dependence -- 3.3 Regularity of the Solution -- 3.4 Random Input Data -- 3.5 Parametric Deterministic Coefficient -- 3.5.1 Deterministic Countably Parametric Elliptic PDEs -- 3.5.2 Probabilistic Setting -- 3.5.3 Deterministic Complex-Parametric Elliptic PDEs -- 3.6 Analyticity and Sparsity -- 3.6.1 Parametric Holomorphy -- 3.6.2 Sparsity of Wiener-Hermite PC Expansion Coefficients -- 3.7 Parametric Hs(D)-Analyticity and Sparsity -- 3.7.1 Hs(D)-Analyticity -- 3.7.2 Sparsity of Wiener-Hermite PC Expansion Coefficients -- 3.8 Parametric Kondrat'ev Analyticity and Sparsity -- 3.8.1 Parametric Ks(D)-Analyticity -- 3.8.2 Sparsity of Ks-Norms of Wiener-Hermite PC Expansion Coefficients -- 3.9 Bibliographical Remarks -- 4 Sparsity for Holomorphic Functions. 327 $a4.1 (b,?,?,X)-Holomorphy and Sparsity -- 4.2 (b,?,?,X)-Holomorphy of Composite Functions -- 4.3 Examples of Holomorphic Data-to-Solution Maps -- 4.3.1 Linear Elliptic Divergence-Form PDE with Parametric Diffusion Coefficient -- 4.3.2 Linear Parabolic PDE with Parametric Coefficient -- 4.3.3 Linear Elastostatics with Log-Gaussian Modulus of Elasticity -- 4.3.4 Maxwell Equations with Log-Gaussian Permittivity -- 4.3.5 Linear Parametric Elliptic Systems and Transmission Problems -- 5 Parametric Posterior Analyticity and Sparsity in BIPs -- 5.1 Formulation and Well-Posedness -- 5.2 Posterior Parametric Holomorphy -- 5.3 Example: Parametric Diffusion Coefficient -- 6 Smolyak Sparse-Grid Interpolation and Quadrature -- 6.1 Smolyak Sparse-Grid Interpolation and Quadrature -- 6.1.1 Smolyak Sparse-Grid Interpolation -- 6.1.2 Smolyak Sparse-Grid Quadrature -- 6.2 Multiindex Sets -- 6.2.1 Number of Function Evaluations -- 6.2.2 Construction of (ck,?)?F -- 6.2.3 Summability Properties of the Collection (ck,?)?F -- 6.2.4 Computing -- 6.3 Interpolation Convergence Rate -- 6.4 Quadrature Convergence Rate -- 7 Multilevel Smolyak Sparse-Grid Interpolation and Quadrature -- 7.1 Setting and Notation -- 7.2 Multilevel Smolyak Sparse-Grid Algorithms -- 7.3 Construction of an Allocation of Discretization Levels -- 7.4 Multilevel Smolyak Sparse-Grid Interpolation Algorithm -- 7.5 Multilevel Smolyak Sparse-Grid Quadrature Algorithm -- 7.6 Examples for Multilevel Interpolation and Quadrature -- 7.6.1 Parametric Diffusion Coefficient in Polygonal Domain -- 7.6.2 Parametric Holomorphy of the Posterior Density in Bayesian PDE Inversion -- 7.7 Linear Multilevel Interpolation and Quadrature Approximation -- 7.7.1 Multilevel Smolyak Sparse-Grid Interpolation -- 7.7.2 Multilevel Smolyak Sparse-Grid Quadrature -- 7.7.3 Applications to Parametric Divergence-Form EllipticPDEs. 327 $a7.7.4 Applications to Holomorphic Functions -- 8 Conclusions -- References -- Index. 330 $aThe present book develops the mathematical and numerical analysis of linear, elliptic and parabolic partial differential equations (PDEs) with coefficients whose logarithms are modelled as Gaussian random fields (GRFs), in polygonal and polyhedral physical domains. Both, forward and Bayesian inverse PDE problems subject to GRF priors are considered. Adopting a pathwise, affine-parametric representation of the GRFs, turns the random PDEs into equivalent, countably-parametric, deterministic PDEs, with nonuniform ellipticity constants. A detailed sparsity analysis of Wiener-Hermite polynomial chaos expansions of the corresponding parametric PDE solution families by analytic continuation into the complex domain is developed, in corner- and edge-weighted function spaces on the physical domain. The presented Algorithms and results are relevant for the mathematical analysis of many approximation methods for PDEs with GRF inputs, suchas model order reduction, neural network and tensor-formatted surrogates of parametric solution families. They are expected to impact computational uncertainty quantification subject to GRF models of uncertainty in PDEs, and are of interest for researchers and graduate students in both, applied and computational mathematics, as well as in computational science and engineering. 410 0$aLecture Notes in Mathematics,$x1617-9692 ;$v2334 606 $aDifferential equations 606 $aProbabilities 606 $aNumerical analysis 606 $aFunctional analysis 606 $aDifferential Equations 606 $aProbability Theory 606 $aNumerical Analysis 606 $aFunctional Analysis 615 0$aDifferential equations. 615 0$aProbabilities. 615 0$aNumerical analysis. 615 0$aFunctional analysis. 615 14$aDifferential Equations. 615 24$aProbability Theory. 615 24$aNumerical Analysis. 615 24$aFunctional Analysis. 676 $a515.35 700 $aD?ng$b Dinh$0768267 701 $aNguyen$b Van Kien$01432777 701 $aSchwab$b Christoph$0342240 701 $aZech$b Jakob$01432778 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910751387703321 996 $aAnalyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs$93577813 997 $aUNINA