Published April 24, 2025 | Version v1
Data paper Open

Rayleigh-Bénard convection data for publication "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

Authors/Creators

  • 1. ROR icon Johns Hopkins University

Contributors

Data collector:

  • 1. ROR icon Johns Hopkins University

Description

This dataset contains the full set of Rayleigh–Bénard convection simulations generated using the Dedalus spectral solver, as described in the Nature Communications article titled "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems." These simulations were used to train and validate the latent neural operator framework introduced in the paper.

Files

Rayleigh-Benard-all-data.zip

Files (9.9 GB)

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Additional details

Software

Repository URL
https://github.com/katiana22/latent-deeponet
Programming language
Python