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Embedding a differentiable fluid simulation into a graph net makes it able to generalize to previously unseen physical behavior, while predicting flows faster than a full simulation. Additionally, it is able to operate directly on CFD meshes, unlike regular convolutional methods.pic.twitter.com/oNli7LPpoe
Glad to see these promising results. Not sure if you have seen our work on using graph neural networks to directly learn the PDEs through operator learning. @kazizzad https://arxiv.org/abs/2003.03485
Nice paper
Thanks @AnimaAnandkumar 4 sharing our paper. I personally like NeuralOperator a lot because of its generality & that it paves the road to learn operators between Banach spaces(got Banach’s biography book as a gift today ;)). Now @filipeabperes’s paper on my priority list 2 read:)
I hadn't seen it yet, I'll definitely take a look! Thanks for the tip
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