Data · dataset · 2026
Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)
Listed in DaRUS
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.
Description
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.
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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 provide videos for the following parameter scans: speed-controller speed-controller (velocity fluctuations) speed-controller, with an integration time step of 2e-3 speed-controller, without external driving (undriven) speed-controller, with a single agent speed-controller, with 500 agents (overdamped phenomenology) speed-controller, with initial transient (burn-in phase) speed-controller, with Hénon-Heiles driving protocol speed-controller, with Rössler driving protocol speed-controller, with Chua driving protocol speed-controller, with Lorenz-96 driving protocol damping analysis, with non-interacting agents damping analysis, with interacting agents alignment force, with speed-controller settings of Lymburn et al. (2021) homing force, with varied speed-controller strength reproduction of the dynamical regimes analyzed in Lymburn et al. (2021) (Fig. 7) 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. doi.org/10.18419/DARUS-4620. Changelog V2 Added videos showing active matter systems with random uniform driving with different driver update intervals (every 1, 2, 5, 10 steps) for parameter combinations of the speed-controller parameter scan, used for the computation of the short-term memory capacity
Links
Where it is published
- Dataverse dataset page darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi%3A10.18419%2FDARUS-4619 ↗
landing page · from darus uni stuttgart de
- DOI doi.org/10.18419/darus-4619 ↗
DOI / persistent id · from darus uni stuttgart de
Catalogue records · 1
- Dataverse API darus.uni-stuttgart.de/api/datasets/:persistentId/?persistentId=doi%3A10.18419%2FDARU… ↗
metadata API · from darus uni stuttgart de
Topics
- Stated by source
- Computer and Information Science · Physics
- From keywords
- Computer Science & AI · Machine learning · Physics · Time-series analysis
- Inferred from text
- Simulation 75% · Video 75%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DaRUS | doi:10.18419/DARUS-4619 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:380205 | mapping · darus uni stuttgart de | vocabulary-mapper@1.0.0 | keywords['Time Series Analysis'] |
| concepts[field].anzsrc:group:4611 | mapping · darus uni stuttgart de | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].dataverse_subject:computer-and-information-science | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].dataverse_subject:physics | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].local:field:physics | mapping · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[method].local:method:simulation | enrichment · darus uni stuttgart de | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:video | enrichment · darus uni stuttgart de | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| description | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /description |
| publication_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| title | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /name |
| updated_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| version_label | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 |