Predicting turbulence with transformers

In this video we explain some of the basic mechanisms of transformers, the deep-learning architectures present in the most novel large language models (LLMs). We discuss their potential to predict turbulent flows, and in particular to reproduce complex temporal dynamics. More information in these references:
- ROMs and transformers: arxiv.org/abs/...
- Inflow conditions and transformers: www.cambridge....
- A new attention mechanism for transformers: arxiv.org/abs/...

[@rcorpchannel]

I heard about your next paper! sounds amazing, good luck!

[@rvinuesa]

Thank you so much!!

[@VamsikrishnaChinta-j1z]

Very interesting talk! Have you tried comparing the performance of your ROM in terms of both prediction time horizon and accuray with other projection-based ROMs such as operator inference? I see that the time horizon of prediction is 50 \Delta t. Is \Delta t DNS time step?

[@rvinuesa]

Excellent question! Yes, we made some comparisons with other methods, see here:
www.nature.com/articles/s41467-024-45578-4
www.sciencedirect.com/science/article/pii/S0142727X23001534