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Dataset for 'Spatiotemporal Chaos Driven by Nonlinear Interactions with Two Critical Wavenumbers'

Citation

Pinkney, Laura, Rucklidge, Alastair M. and Beaume, Cédric (2026) Dataset for 'Spatiotemporal Chaos Driven by Nonlinear Interactions with Two Critical Wavenumbers'. University of Leeds. [Dataset] https://doi.org/10.5518/1819

Dataset description

The dataset contains the data for generating the figures in the paper. The code directory contains the python codes required to generate the figures. The remaining two directories contain the files required to execute these codes. To generate the figures, only PDE_pattern_classification.py needs to be executed. This imports data from 'output_files' to do this. However, if the user would like to generate data given in 'output_files' themselves, then the other python codes are required. The files in 'input_files' are required to execute ETD4RK.py for the first value of Q_1 (Q_1=-1.4). For the remaining values of Q_1 a different initial condition will need to be imported (to generate the same figures presented in the paper). This initial condition will be the final solution generated for the previous value of Q_1, which should be saved as 'new_U_ic.txt' (this is saved by default when executing ETD4RK.py). The files in 'output_files' can be generated by executing loadandplot_newcriteria.py (after executing ETD4RK.py) for a range of values of Q_1 which are named in the subdirectories of 'output_files'. The data within these subdirectories are the inputs for PDE_pattern_classification.py which generates the figures in the paper. A sample file tree to describe the paths defined within the codes is also included.

Keywords: pattern formation, three-wave interactions, resonant triads, spatiotemporal chaos
Subjects: G000 - Mathematical sciences > G100 - Mathematics > G120 - Applied mathematics
Divisions: Faculty of Engineering and Physical Sciences > School of Mathematics
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited: 27 May 2026 13:02
URI: https://archive.researchdata.leeds.ac.uk/id/eprint/1552

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