Replication Data for: CODE: A global approach to ODE dynamics learning

Version 1.0
Wildt, Nils, 2026, "Replication Data for: CODE: A global approach to ODE dynamics learning", https://doi.org/10.18419/DARUS-5826, DaRUS, V1
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Description

Pre-computed experimental results for the paper "CODE: A global approach to ODE dynamics learning" (Wildt, Tartakovsky, Oladyshkin, Nowak, JMLMC 2025).

The dataset contains trained model results (JLD2 files) from three data-driven ODE learning approaches — arbitrary polynomial chaos expansion (aPCE/chaos), neural networks (Lux), and Gaussian process kernel regression (GP) — applied to two benchmark dynamical systems:

  • Lotka–Volterra (predator–prey): 750 experiment runs + 234 orthonormal comparison runs (984 JLD2 files, ~13 GB)
  • Damped Pendulum: 45 experiment runs including neural network model files (60 JLD2 files, ~88 MB)

Each JLD2 file stores one fully trained model together with its predictions, extrapolations, out-of-distribution results, training losses, and the corresponding experiment configuration. Experiments systematically vary training data size (N_data), observation noise level, and random seed.

Reproduction: Clone the companion repository and run the plot scripts to reproduce all 25 paper figures.
Repository: https://github.com/NilsWildt/CODE-Global-ODE-learning

(2026-03-30)
Subject Computer and Information Science; Engineering; Mathematical Sciences
Keyword ODE Learning, Arbitrary Polynomial Chaos, Kernel ODE, Chaos ODE, Data-Driven Modeling
Related Publication Is Supplement To: N. Wildt, D. M. Tartakovsky, S. Oladyshkin, and W. Nowak, "Code: A Global Approach to ODE Dynamics Learning," J. Mach. Learn. Model. Comput., vol. 7, no. 2, pp. 73-105, 2026.doi: 10.1615/JMachLearnModelComput.2026062518
Notes Together with https://github.com/NilsWildt/CODE-Global-ODE-learning Each JLD2 file is one experiment run. Filenames encode all hyperparameters (ansatz, N_data, noise, seed, etc.).
The companion repository scripts/03_plot_lv_figures.jl and scripts/04_plot_pendulum_figures.jl read these files to produce the 25 paper figures.
License/Data Use Agreement

CC BY 4.0

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results_LV.tar.gz
results/Gzip Archive - 8.1 GB
Published Aug 13, 2026
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MD5: 16ce1e185081ed369271e7b274369e38
Lotka-Volterra results data
results_Pendulum.tar.gz
results/Gzip Archive - 46.3 MB
Published Aug 13, 2026
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MD5: 5b95fd4215e1cf33fc6e312b8112b2e0
Pendulum results data
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Curation Status History
StatusDateAssigner
2026-08-13 14:48:25Bernd Flemisch
Waiting for Feedback from Author2026-07-28 12:07:47Dataverse Admin
In Review by FoKUS2026-07-27 16:22:45Dataverse Admin
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