NurshatMenglik/NeuralConvection_2D_TrainTest

NeuralConvection — 2D Training/Test Data

📄 Paper: A Neural Surrogate Approach for Simulating Natural Convection Problems (arXiv:2606.25259) — Nurshat Menglik, Alex Shao, David Hyde.

Paired 2D Boussinesq -> compressible natural-convection fields used to train and evaluate the Fourier Neural Operator (FNO) surrogate in A Neural Surrogate Approach for Simulating Natural Convection Problems.

Each split provides two tensors:

• X.pt — Boussinesq input, shape (N, 4, 64, 64), float32
• Y.pt — compressible target, shape (N, 4, 64, 64), float32

Channels (both X and Y): [u_x, u_y, T, p] on a 64x64 grid. kept_samples.txt lists the source sample IDs for each split.

Splits

Folder	N	Description
train/	4000	Training set (stochastic Fourier boundary-condition generator)
test_legacy/	200	Legacy constant-temperature-wall test set
test_in_distribution/	200	In-distribution stochastic test set
test_ood/	200	Out-of-distribution test set (wider Rayleigh-number and BC ranges)

Source

Generated with the solver and data-generation pipeline at https://github.com/Nurshat317/NeuralConvection

License

Released under CC BY 4.0. You are free to share and adapt this data, including commercially, provided you give appropriate credit and indicate any changes. The requested form of credit is a citation to the paper below.

Citation

If you use this dataset in your research, please cite:

@article{menglik2026neural,
  title         = {A Neural Surrogate Approach for Simulating Natural Convection Problems},
  author        = {Menglik, Nurshat and Shao, Alex and Hyde, David},
  journal       = {arXiv preprint arXiv:2606.25259},
  year          = {2026},
  eprint        = {2606.25259},
  archivePrefix = {arXiv},
  primaryClass  = {physics.comp-ph},
  url           = {https://arxiv.org/abs/2606.25259}
}

• Paper: arXiv:2606.25259
• Code: github.com/Nurshat317/NeuralConvection