Fast simulation of Gaussian random fields
arXiv:1105.2737 · doi:10.1515/MCMA.2011.009
Abstract
Fast Fourier transforms are used to develop algorithms for the fast generation of correlated Gaussian random fields on d-dimensional rectangular regions. The complexities of the algorithms are derived, simulation results and error analysis are presented.
15 pages, 8 figures. Typos corrected in Algorithm 3, Remark (4), Algorithm 4, Remark (5), and Algorithm 5, Remark (5)
Cited by in corpus (14)
- Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models
- Isotropic Gaussian random fields on the sphere: Regularity, fast simulation and stochastic partial differential equations
- Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling
- Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
- Predicting permeability via statistical learning on higher-order microstructural information
- Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification
- Spontaneous Symmetry Breaking for Extreme Vorticity and Strain in the 3D Navier-Stokes Equations
- A Multiscale Guide to Brownian Motion
- Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation
- Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization
- Hilbert--Schmidt regularity of symmetric integral operators on bounded domains with applications to SPDE approximations
- Instantons, fluctuations and singularities in the supercritical stochastic nonlinear Schroedinger equation
- Isotropic Q-fractional Brownian motion on the sphere: regularity and fast simulation
- Uncertainty quantification for hyperbolic conservation laws with flux coefficients given by spatiotemporal random fields