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5 results for “Chaotic Systems”
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Machine Learning for predicting chaotic systems – Data
<p>The data used in our article "Machine Learning for Predicting Chaotic Systems" - <a href="https://arxiv.org/abs/2407.20158">https://arxiv.org/abs/2407.20158</a></p> <p>DeebDbDysts*.zip contain the Dysts database, DeebDbLorenz*.zip the DeebLorenz database (with DeebDbLorenzBig*.zip being the "extension" dataset for Lorenz63std with different time series lengths).</p> <p>The observation and truth data of the Dysts database originates from <a href="https://github.com/williamgilpin/dysts">https://github.com/williamgilpin/dysts</a> (we converted the data format from json to csv).</p> <p>For DeebLorenz, we used the R package <a href="https://github.com/chroetz/DEEBdata">DEEBdata</a> to create it.</p>
Learning Dissipative Dynamics in Chaotic Systems (Datasets)
<p>We present the datasets for NeurIPS 2022 paper <a href="https://arxiv.org/abs/2106.06898">"Learning Dissipative Dynamics in Chaotic Systems."</a> In this work, we propose a machine learning framework, which we call the Markov Neural Operator (MNO), to learn the underlying solution operator for dissipative chaotic systems, showing that the resulting learned operator accurately captures short-time trajectories and long-time statistical behavior.</p> <p>In our work, we present results in the finite-dimensional toy system Lorenz-63. We showcase results on the 1D Kuramoto–Sivashinsky (KS) and on the 2D Navier-Stokes (Kolmogorov flows) PDEs. We present the datasets for Lorenz-63, KS, and Navier-Stokes (Reynolds numbers 40, 500, and 5000).</p> <p>The data is stored as .npy and .mat files:</p> <ul> <li><strong>L63.mat: </strong>Lorenz-63 data (one long trajectory of 10000 seconds)</li> <li><strong>KS.mat:</strong> 1D Kuramoto–Sivashinsky data (1200 trajectories, 500 time-steps each)</li> <li><strong>2D_NS_Re40.npy: </strong>2D Navier-Stokes data (200 trajectories, 500 time-steps each) at 64 x 64 spatial resolution.</li> <li><strong>2D_NS_Re500.npy: </strong>2D Navier-Stokes data (1000 trajectories, 500 time-steps each) at 64 x 64 spatial resolution with Reynolds number 500.</li> <li><strong>2D_NS_Re5000.npy: </strong>2D Navier-Stokes data (100 trajectories, 500 time-steps each) at 128 x 128 spatial resolution with Reynolds number 5000.</li> </ul>
Time series of chaotic systems
<div> <div> </div> </div> <div> <p>Long time series of chaotic systems, all three-dimensional. Can be used in short- and long-term forecasting, reconstruction, etc.</p> <p>Codes in GitHub: https://github.com/Zheng-Meng/Dynamics-Reconstruction-ML.</p> <p>We used the dataset in dynamics reconstruction from sparse observations with no training on target systems:</p> <p>Zhai, Zheng-Meng, Jun-Yin Huang, Benjamin D. Stern, and Ying-Cheng Lai. "Reconstructing dynamics from sparse observations with no training on target system." <em>arXiv preprint arXiv:2410.21222</em> (2024).</p> <p>In addition, two folders with additional data, data_response, which is generated by dysts (https://github.com/williamgilpin/dysts) and data_nonautonomous, are provided for further evaluation of the dynamics reconstruction framework.</p> <p> </p> </div>
Chaotic Dynamics in a Two-Droplet Pilot Wave System: A Numerical Simulation
<p>Data set and results for our university modeling project.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.