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Top-Avocado-2564 t1_ixcrq8m wrote

PiNN aren't really supervised or unsupervised so to speak. It's a misleading way to think about PiNN architecture

Neural pde solvers can be of three flavours - operator learning, graph pde and purely function approximator ( lagaris 2007 ) approach.

SOTA in pinns is a bit useless. Nobody cares if you can do burgers equation as fast as possible. Real life systems are coupled, mixture of pde/ ode , possibly stiff, it's a smorgasbord of challenges.

Fno works great in some situations but it has limitations in handling stochastic multiscale systems - think high RANS

When it comes to PiNN ymmv

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a1_jakesauce_ OP t1_ixcz153 wrote

I’m looking for competitors for FNO - any recommendations for models that would be a fair comparison?

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Top-Avocado-2564 t1_ixd75dm wrote

Try deepOnets, but honestly without knowing specifics of your problem. It is hard to advise, PiNN is different from classic DL

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a1_jakesauce_ OP t1_ixdi3b9 wrote

I want to compare FNO to other SOTA that have been published since ICLR 21 on the 2d NS task from the FNO paper - that is, predict the scalar vorticity of NS on a regular spatial temporal grid for 20-40 time steps into the future given the first 10 solutions

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