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1/#Neural ODE models are ideal for #AI4science since physical processes are described by differential equations. #Neural ODEs find latent space representations for forecasting #turbulence If you fly regularly like me, you know this is important ;) #AI #fluiddynamics #DeepLearning
2/ to be presented at @NeurIPSConf ML4PS and APS-DFD With @peetak_mitra @TanNguyen689 @animesh_garg
https://arxiv.org/abs/1911.05180 #AI #fluiddynamics #DeepLearning #AI4science #AI4physicspic.twitter.com/u4pW0MLT1O
But analytic models still give us the best understanding of the phenomena; so 'ideal' is a bit far-fetched.
This is still analytic in a latent space. It is exploiting low dimensionality in data while also incorporating differential equations for evolution. Hence the term..ideal
@ArchanaIyer1996 This looks really interesting! Check it out
Once again reinforcing what since Lagrangian mechanics we know, the way you encode a problem is crucial for the ease, or not, it can be solved.
Looks very nice!
Are there any issues related to the loss of precision during the integration process? I imagine that such systems can easily become chaotic.
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