Extensions of the Novikov-Furutsu theorem, obtained by using Volterra functional calculus

Novikov-Furutsu (NF) theorem is a well-known mathematical tool, used in stochastic dynamics for correlation splitting, that is, for evaluating the mean value of the product of a random functional with a Gaussian argument multiplied by the argument itself. In this work, the NF theorem is extended for mappings (function-functionals) of two arguments, one being a random variable and the other a random function, both of which are Gaussian, may have non-zero mean values, and may be correlated with each other. This extension allows for the study of random differential equations under coloured noise excitation, which may be correlated with the random initial value. Applications in this direction are briefly discussed. The proof of the extended NF theorem is based on a more general result, also proven herein by using Volterra functional calculus, stating that: The mean value of a general, nonlinear function-functional having random arguments, possibly non-Gaussian, can be expressed in terms of the characteristic functional of its arguments. Generalizations to the multidimensional case (multivariate random arguments) are also presented.