Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far-reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly effective and interpretable control functions for such systems.

We present Glyph-a Python package for genetic programming based symbolic regression. Glyph is designed for usage in numerical simulations as well as real world experiments. For experimentalists, glyphremote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at https://github. com/Ambrosys/glyph. Domain experts are able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a generic interface design. Glyph is available on PyPI and Github.

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This document describes a plug-in for Blender (www.blender.org) that allows to rasterize 3D mesh objects into 3D voxel data, i. e. it allows to voxelize Blender meshes. In 2D, this process can be compared to rasterization of vector graphics into pixel graphics. The voxelization is done by VTK (www.vtk.org) functions. A simple GUI allows to choose the type of voxelization and to specify necessary parameters. Depending on the type chosen, only the surface of the mesh objects is voxelized or the enclosed volume, i. e.“filled”. Beside the size of the output, one can specify if the result should be “anti-aliased” or not. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the script described in this paper.