%0 Generic %1 gaimann2025supplementary %A Gaimann, Mario U. %A Klopotek, Miriam %D 2025 %K %R 10.18419/darus-4619 %T Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025) %X This dataset contains supplementary videos for the publication "Robustly optimal dynamics for active matter reservoir computing" (Gaimann and Klopotek, 2025). 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. Their default external driving protocol is the chaotic attractor Lorenz-63, but we also employ the attractors Hénon-Heiles, Rössler, Chua, and Lorenz-96 as benchmarks. 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 alignment, local repulsion, global attraction (homing) to the center of the simulation box, speed control towards a constant agent speed, and local driver repulsion. 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. 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 also provide visualizations of the time evolution of chaotic attractors that we use as driving protocols: Lorenz-63, Hénon–Heiles, Rössler, Chua, Lorenz-96. The raw data used to generate these videos is published as: Gaimann, M. U., & Klopotek, M. (2025). Replication Data for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025). DaRUS. https://doi.org/10.18419/DARUS-4620.

@misc{gaimann2025supplementary, abstract = {This dataset contains supplementary videos for the publication "Robustly optimal dynamics for active matter reservoir computing" (Gaimann and Klopotek, 2025). 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. Their default external driving protocol is the chaotic attractor Lorenz-63, but we also employ the attractors Hénon-Heiles, Rössler, Chua, and Lorenz-96 as benchmarks. 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 alignment, local repulsion, global attraction (homing) to the center of the simulation box, speed control towards a constant agent speed, and local driver repulsion. 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. 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 also provide visualizations of the time evolution of chaotic attractors that we use as driving protocols: Lorenz-63, Hénon–Heiles, Rössler, Chua, Lorenz-96. The raw data used to generate these videos is published as: Gaimann, M. U., & Klopotek, M. (2025). Replication Data for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025). DaRUS. https://doi.org/10.18419/DARUS-4620. }, added-at = {2025-05-19T09:44:00.000+0200}, affiliation = {Gaimann, Mario U./University of Stuttgart, Klopotek, Miriam/University of Stuttgart}, author = {Gaimann, Mario U. and Klopotek, Miriam}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/241a8ab2e5cee991af9c95a0e7268b398/unibiblio}, doi = {10.18419/darus-4619}, howpublished = {Dataset}, interhash = {bcda5d510e4a9cf34b055419c875e8c8}, intrahash = {41a8ab2e5cee991af9c95a0e7268b398}, keywords = {}, note = {Related to: Gaimann, M. U., & Klopotek, M. (2025). Robustly optimal dynamics for active matter reservoir computing. arXiv: 2505.05420}, orcid-numbers = {Gaimann, Mario U./https://orcid.org/0000-0002-2789-090X, Klopotek, Miriam/https://orcid.org/0000-0001-9174-1282}, timestamp = {2025-05-19T07:44:00.000+0200}, title = {Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)}, year = 2025 }