Downloads
Swarmsim: agent-based simulation of Mobile agents
Simulate 2D and 3D swarms of mobile agents with various dynamics and implement distributed control laws to obtain new behaviours.
Features:
Simulate swarms of mobile agents.
Implement your own dynamical model or use one of the embedded ones.
Implement your own distributed control law or use one of the embedded ones.
Acquisition of metrics to evaluate the performance.
Extensive simulations to study stochastic effects or different initial conditions.
Extensive simulations to study the effects of the parameters.
Local stability analysis through linearization.
For further details see: SwarmSim
Chronos: A software tool to study synchronization and coordination in human ensembles
Coordination and synchronization between the motion of two or more individuals are common yet complex phenomenona taking place in several daily activities. The mirror game has been recently proposed as a simple, yet powerful paradigm for studying interpersonal human coordination between two participants.
Chronos implements an extension of the mirror game to multiplayer scenarios. Not only, it allows one or more people to play with each others, but some of the players can be replaced with one or more virtual agents.
For further details see:
Study of movement coordination in human ensembles via a novel computer-based set-up. Alderisio F, Lombardi M, Fiore G, di Bernardo M. Available arXiv preprint: arXiv:1608.04652; (2016). [Read paper]
BSim: Agent-based modelling tool for bacterial populations in Systems and Synthetic Biology
BSim is an agent-based modelling tool designed to allow for the study of bacterial populations. By enabling the description of bacterial behaviours, it attempts to provide an environment in which to investigate how local interactions between individual bacterium leads to the emergence of population level features, such as cooperation and synchronisation.
For further details click the following link:
Gorochowski, T. E., Matyjaszkiewicz, A., Todd, T., Oak, N., Kowalska, K., Reid, S., ... & Di Bernardo, M. (2012). BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology. PLoS ONE, 7(8), e42790
NetEvo: Tools for complex dynamical network evolution
NetEvo is a computing framework and collection of end-user tools designed to allow researchers to investigate evolutionary aspects of dynamical complex networks.
To be able to study the behaviour of complex systems NetEvo provides two main functions, simulation and evolution of dynamical networks brought together in a coherent way using the idea of a supervisor.
Algorithm for computing basins of attraction in Filippov systems
Filippov systems are often used for modeling mechanical, electrical and biological systems. The computation of their basins of attraction is a very relevant problem in applications; for example, it provides useful information about complex behavior caused by friction impacts or damping, in the design of mechanical devices.
The ESCM is a numerical tool for studying the basins of attraction in Filippov systems with a single discontinuity manifold. The algorithm is an extension of the Simple Cell Mapping method for discontinuous systems, implemented in a Matlab environment.
For further details see:
Dynamic cell mapping algorithm for computing basins of attraction in Filippov systems, International Journal and Bifurcations (IJBC) (Submitted), 2017.
The Matlab code can be downloaded from here:
REDRAW: an algorithm to reconstruct directed and weighted topologies
The formalism of complex networks is extensively employed to describe the dynamics of interacting agents in several applications. The
features of the connections among the nodes in a network are not always provided beforehand, hence the problem of appropriately inferring them often arises. Here, we present a method to reconstruct directed and weighted topologies (REDRAW) of networks of heterogeneous nonlinear oscillators, and illustrate the theory on a set of representative examples.
For further details see:
Alderisio, F., Fiore, G., & di Bernardo, M. (2017). Reconstructing the structure of directed and weighted networks of nonlinear oscillators. Physical Review E, 95(4), 042302.
The Matlab code of REDRAW can be downloaded from here:
