Abstract
A methodology is presented for simulation of strongly non-Gaussian random fields. It involves an iterative scheme that produces sample functions that match a prescribed non-Gaussian marginal distribution and a prescribed Spectral Density Function (SDF). The simulated field possesses all the properties of translation fields. The methodology also determines the SDF of an underlying Gaussian field according to translation field theory. This is the latest development in a class of simulation algorithms that are presented and critically reviewed. Several numerical examples are provided demonstrating the capabilities of the methodology, comparing it with three previous algorithms, and determining the limits of its applicability. Compared to earlier algorithms, the proposed methodology provides increased accuracy at a fraction of the computational cost.
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.
