Thank you @MCHammer for retweeting our work on Fourier neural operators!
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Blog post by
@ZongyiLiCaltech https://zongyi-li.github.io/blog/2020/fourier-pde/ …@kazizzad@CaltechShow this thread
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Hi. How flexible/sensitive is this framework to the changes in geometries and meshes? For example, say I want to add/remove a feature, like a hole or a fillet, or place an obstacle to divert the flow or change the mesh resolution adaptively in an unsteady simulation.
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This learns the operator of PDE. So it doesn't rely on mesh and is not resolution dependent. We see high fidelity super resolution capability in our method.
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Will you have a video taped seminar of this. I tried reading the paper and lets just say I’m more of a visual learner?
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Actually just to clarify, I’d be happy just understanding the original paper, neural operators: Graph Kernel Network... Hopefully I’ll be able to figure out the extensions from understanding that one.
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Interesting work.
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Thank you ! We work with CNNs in the Fourier domain
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This is actually pretty amazing! Are there any particularly interesting applications you envision for this method?
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There are so many interesting ones, so many that I can't count ;) Basically, solutions to problems that require mappings from function spaces (or generally infinite-d spaces) may benefit from this.
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