Supplementary Videos for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)

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This dataset contains supplementary videos for the publication "Optimal information injection and transfer mechanisms for active matter reservoir computing" (Gaimann and Klopotek, 2025) (to be published).
The datasets contain physical observables recorded during non-equilibrium simulations of active matter systems (swarms) driven by an external force. These simulations serve as information processors in a reservoir computing setup.
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<p>
The videos show active matter systems (swarms) driven by an external force. These swarm systems can be used to predict the future trajectory of the external driving force using reservoir computing. We use the chaotic attractor Lorenz-63 as the external driving protocol and as a benchmark. Agents are colored by their current speed. The driver is marked as a black spiked ball, follows a fixed trajectory specified by the driving protocol, and exerts a repulsive force on the agents. The past positions of agents and drivers in a time window of 0.1 time units (5 integration time steps of 0.02 time units as default) are displayed as traces. Agents experience local repulsion, global attraction (homing) to the center of the simulation box, speed control towards a constant agent speed, and local driver interaction. Specifically, in this work, we present simulations with two types of attractive drivers (linear and inverse). A sigmoid force clamp (wrapper) processes and limits the total force experienced by each agent. The simulation uses periodic boundary conditions. Velocity fluctuations are colored by their orientation; the green cross indicates the center of mass. By default, we use 200 agents.
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Each video corresponds to a specific parameter combination or a point in a parameter scan presented in the corresponding publication, or to a specific parameter combination. We provide videos for the following parameter scans:
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<ul>
<li>speed-controller scan, with inversely attractive driver</li>
<li>speed-controller scan, with inversely attractive driver (velocity fluctuations)</li>
<li>speed-controller scan, with linearly attractive driver</li>
<li>speed-controller scan, with linearly attractive driver (velocity fluctuations)</li>
<li>driver repulsion scan, near-critical speed-controller setting</li>
<li>driver repulsion scan, near-critical speed-controller setting, single agent</li>
<li>driver repulsion scan, Lymburn et al. (2021) speed-controller setting</li>
<li>inverse driver attraction scan, near-critical, single agent</li>
<li>agent-agent repulsion scan, with a repulsive driver</li>
<li>agent-agent repulsion scan, with an inversely attractive driver</li>
<li>inverse driver attraction scan, near-critical speed-controller setting</li>
<li>agent repulsion strength vs. number of agents scan, with a repulsive driver</li>
<li>agent repulsion strength vs. number of agents scan, with an inversely attractive driver</li>
<li>agent repulsion strength vs. number of agents scan, with an inversely attractive driver, with a repulsion radius of 1.0 and a driver strength of 100.0</li>
<li>agent repulsion strength vs. number of agents scan, with an inversely attractive driver, with a repulsion radius of 1.0 and a driver strength of 11.2883789</li>
<li>viscoelastic fluids</li>
<li>undriven system, near-critical speed-controller setting</li>
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The raw data used to generate these videos is published as: Gaimann, M. U., & Klopotek, M. (2025). Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025). DaRUS. https://doi.org/10.18419/DARUS-4805.
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<p>Changelog</p>

<p>V2</p>
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<ul>
<li>Added videos showing active matter systems with different driver speeds (1.0, 2.0, 5.0, 10.0, 15.0) for parameter combinations of the speed-controller parameter scan</li>
</ul>
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