It takes the average reader 3 hours and 41 minutes to read Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs by Dinh Dũng
Assuming a reading speed of 250 words per minute. Learn more
The 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, such as 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.
Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs by Dinh Dũng is 216 pages long, and a total of 55,296 words.
This makes it 73% the length of the average book. It also has 68% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 5 hours and 2 minutes to read Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs aloud.
Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs is suitable for students ages 12 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
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