Backward differential formulae (BDF) are the basis of the highly efficient schemes for the
numerical solution of stiff ordinary differential equations for decades. An alternative multistep …

This study addresses the accuracy of stochastic simulations performed in Two-Dimensional
Stochastic Neural Fields (2D-SNFs) with the infinite signal transmission speed and in the …

This paper advances further the idea of overall continuous–discrete Gaussian filtering with
deterministically sampled expectation and covariance towards efficient mixed-type accurate …

This paper continues our research devoted to an accurate nonlinear Bayesian filters' design.
Our solution implies numerical methods for solving ordinary differential equations (ODE) …

This paper aims at devising reliable state estimation means in the stochastic integro-
differential Amari equation simulating neural field activities in the presence of external …

We introduce multivalue second derivative collocation methods for the numerical solution of
stiff ordinary differential equations, also arising from the spatial discretization of time …

The paper deals with the construction of explicit Nordsieck second derivative general linear
methods with s stages of order p with and high stage order with inherent Runge–Kutta or …

Algebraic stability has been already studied for general linear methods (GLMs) to analyze
the behavior of the methods applying to the non-linear problems. We discuss this concept for …

This chapter presents a sound insight into the theory of unscented Kalman filtering for
continuous–discrete stochastic systems. In particular, it gives precise definitions of the …

This chapter presents a sound insight into the theory of Kalman filtering with deterministically
sampled mean and covariance for continuous–discrete stochastic systems. In particular, it …