Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs

Discover the intricate world of uncertainty quantification with "Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs" by Dinh Dũng. Published by Springer International Publishing AG in 2023, this insightful first edition spans 207 pages, offering a comprehensive exploration of mathematical and numerical analysis related to linear, elliptic, and parabolic partial differential equations (PDEs).

Dinh Dũng expertly delves into the complexities of PDEs, specifically focusing on coefficients modeled as Gaussian random fields (GRFs) within polygonal and polyhedral physical domains. This book is an essential resource for researchers and practitioners seeking to enhance their understanding of stochastic modeling and its applications in various fields. Whether you're a student or a seasoned professional, this work will equip you with valuable insights into the interplay between analyticity and sparsity in uncertainty quantification.