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Simulation data: Data-driven model reduction of agent-based systems using the Koopman generator

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Metadaten
Author:Jan-Hendrik Niemann, Christof Schütte, Stefan Klus
Document Type:Research data
Resource Type General:Dataset
Parent Title (English):PLOS ONE
Volume:16
Issue:5
Year of first publication:2021
Notes:
This repository contains the simulation data for the article "Data-driven model reduction of agent-based systems using the Koopman generator" by Jan-Hendrik Niemann, Stefan Klus and Christof Schütte. The archive complete_voter_model.zip contains the simulation results for the extended voter model on a complete graph for the parameters given in the corresponding txt-files to learn a reduced SDE model. The files are of the form [types, time steps, samples, training points].The archive dependency.zip contains additional simulation results of the form [types, time steps, samples, training points] to learn a reduced SDE model. The parameters used are given in the corresponding txt-files.The archive random_voter_model.zip contains the simulation results to learn a reduced SDE model for the given adjacency matrix within the archive. The file aggregate_state is of the form [training points, types, time steps, samples]. The file full_state is of the form [training points, agents, time steps, samples].The archive predator_prey_model.zip contains the simulation results to learn a reduced SDE model and calculation of the mean value of the agent-based model. The data is of the form [types, time steps, samples, training points] and [samples, time steps, types].The archive two_clustered_voter_model.zip contains the simulation results for the extended voter model on a graph with two clusters for the given adjacency matrices to learn a reduced SDE model. The file aggregate_state is of the form [training points, types, time steps, samples]. The file full_state is of the form [training points, agents, time steps, samples].
Related Identifier:https://doi.org/10.1371/journal.pone.0250970
Download Url:https://zenodo.org/record/4522119#.YUB1gS223mE
DOI:https://doi.org/http://doi.org/10.5281/zenodo.4522119
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
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