A unified IMEX Runge-Kutta approach for hyperbolic systems with multiscale relaxation

S. Boscarino, L. Pareschi, G. Russo (16/1/2017 preprint arXiv:1701.04370)

In this paper we consider the development of Implicit-Explicit (IMEX) Runge-Kutta schemes for hyperbolic systems with multiscale relaxation. In such systems the scaling depends on an additional parameter which modifies the nature of the asymptotic behavior which can be either hyperbolic or parabolic. Because of the multiple scalings, standard IMEX Runge-Kutta methods for hyperbolic systems with relaxation loose their efficiency and a different approach should be adopted to guarantee asymptotic preservation in stiff regimes.

On the asymptotic properties of IMEX Runge–Kutta schemes for hyperbolic balance laws

Sebastiano Boscarino, Lorenzo Pareschi
(5/9/2016 to appear in Journal of Computational and Applied Mathematics)

Implicit–Explicit (IMEX) schemes are a powerful tool in the development of numerical methods for hyperbolic systems with stiff sources. Here we focus our attention on the asymptotic properties of such schemes, like the preservation of steady-states (well-balanced property) and the behavior in presence of small space–time scales (asymptotic preservation property).

Implicit-explicit linear multistep methods for stiff kinetic equations

Giacomo Dimarco, Lorenzo Pareschi
(1/3/2016 preprint arxiv: 1603.00102)

We consider the development of high order asymptotic-preserving linear multistep methods for kinetic equations and related problems. The methods are first developed for BGK-like kinetic models and then extended to the case of the full Boltzmann equation. The behavior of the schemes in the Navier-Stokes regime is also studied and compatibility conditions derived.

Selective model-predictive control for flocking systems

Giacomo Albi, Lorenzo Pareschi
(5/10/2016 preprint arxiv: 1603.05012)

In this paper the optimal control of alignment models composed by a large number of agents is investigated in presence of a selective action of the control. As a first step toward a reduction of computational cost, we introduce a model predictive control (MPC) approximation by deriving a numerical scheme with a feedback selective constrained dynamics.