Abstract
The statistical inverse problem for the experimental identification of a non-Gaussian matrix-valued random field that is the model parameter of a boundary value problem, using some partial and limited experimental data related to a model observation, is a very difficult and challenging problem. A complete advanced methodology is presented and is based on the use of all the developments presented in the previous chapters and in particular, the random matrix theory (Chapter 5 ), the stochastic solvers (Chapters 4 and 6 ), the statistical inverse methods (Chapter 7 ). However, we will start this chapter by presenting new mathematical results concerning the random fields and their polynomial chaos representations, which constitute the extension in infinite dimension of the tools presented in Sections 5.5 to 5.7 for the finite dimension, and which are necessary for solving the statistical inverse problems related to the non-Gaussian random fields.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
