
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.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
