Overview
- There is no similar text, at present, where sparsity forward and inverse UQ for these PDEs can be currently found
- About subsurface flow, linearly elastic deformations of random medium in solid mechanics, time-harmonic electromagnetics
- Applications to math. of computational PDE uncertainty quantification, comp. sci. and engineering, approximation theory
Part of the book series: Lecture Notes in Mathematics (LNM, volume 2334)
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About this book
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.
Keywords
Table of contents (8 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
Van Kien Nguyen is currently a lecturer in the Department of Mathematical Analysis, University of Transport and Communications, Hanoi, Vietnam. He graduated from University of Science, Vietnam National University Hanoi. He obtained his PhD in Mathematics from Friedrich Schiller University Jena, Germany. His areas of interest are function spaces, approximation theory, and numerical analysis.
Christoph Schwab is professor of mathematics at the Seminar for Applied Mathematics at ETH Zurich. His areas of research encompass Numerical Analysis of Partial Differential Equations, in particular Finite- and Boundary Element Methods, and the mathematical investigation of numerical methods for high-dimensional PDEs, with emphasis on forward and Bayesian inverse problems in numerical Uncertainty Quantification. His results are published in numerous articles in major journals in applied and computational mathematics. He was speaker at ICM2002, and PI of an ERC advanced grant.
Jakob Zech currently holds a position as Assistant Professor for Machine Learning in Scientific Computing at Heidelberg University. His academic path started at TU Wien, where he earned his Bachelor's degree in 2012. He then continued his studies at ETH Zurich, completing his Masters in 2014 and his PhD in Mathematics in 2018. His PhD research revolved around the approximation of high-dimensional parametric PDEs. Upon obtaining his PhD, he received the Early PostdocMobility fellowship from the Swiss National Science Foundation and spent a year as a Postdoc at the Massachusetts Institute of Technology in 2019. Subsequently he joined Heidelberg University in April 2020. His research interests include high-dimensional approximation, deep learning theory, statistical inference, uncertainty quantification, and numerics of PDEs, which resulted in numerous publications in top-tier academic journals.Bibliographic Information
Book Title: Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs
Authors: Dinh Dũng, Van Kien Nguyen, Christoph Schwab, Jakob Zech
Series Title: Lecture Notes in Mathematics
DOI: https://doi.org/10.1007/978-3-031-38384-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Softcover ISBN: 978-3-031-38383-0Published: 14 October 2023
eBook ISBN: 978-3-031-38384-7Published: 13 October 2023
Series ISSN: 0075-8434
Series E-ISSN: 1617-9692
Edition Number: 1
Number of Pages: XV, 207
Topics: Analysis, Probability Theory and Stochastic Processes, Numerical Analysis, Functional Analysis