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AnimaAnandkumar's profile
Prof. Anima Anandkumar
Prof. Anima Anandkumar
Prof. Anima Anandkumar
@AnimaAnandkumar

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Prof. Anima Anandkumar

@AnimaAnandkumar

Director of #AI #research @nvidia, Bren #Professor @Caltech, Fmr Principal scientist @awscloud #Sloan fellow #Tensors Erdos #2 #dancer http://anima-ai.org 

Santa Clara, CA
tensorlab.cms.caltech.edu/users/anima/
Joined October 2014

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    Prof. Anima Anandkumar‏ @AnimaAnandkumar Oct 21
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    Fourier neural operator for PDEs https://arxiv.org/abs/2010.08895  Solves family of #PDE from scratch at any resolution. Outperforms all existing #DeepLearning methods. 1000x faster than traditional solvers Experiments on Navier-Stokes equations @kazizzad @Caltech #AI #HPC https://twitter.com/arXiv_Daily/status/1318645827460603904 …pic.twitter.com/oOS0EiLdwd

    4:19 PM - 21 Oct 2020
    • 463 Retweets
    • 1,911 Likes
    • Jannis Fengler ecogyenergy CloudDev Chris Arash The Drunken Moose 👽🚀👾🛸✈️🚁🛩️ Moha Esla Maura 🇮🇹🇪🇺🐟#facciamorete Rohit Abtean
    30 replies 463 retweets 1,911 likes
      1. New conversation
      2. Prof. Anima Anandkumar‏ @AnimaAnandkumar Oct 22
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        Thank you @MCHammer for retweeting our work on Fourier neural operators!

        3 replies 12 retweets 130 likes
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      3. Prof. Anima Anandkumar‏ @AnimaAnandkumar Nov 11
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        Blog post by @ZongyiLiCaltech https://zongyi-li.github.io/blog/2020/fourier-pde/ … @kazizzad @Caltech

        0 replies 0 retweets 4 likes
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      4. End of conversation
      1. New conversation
      2. Chenna‏ @chenna1985 Oct 21
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        Replying to @AnimaAnandkumar @kazizzad @Caltech

        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.

        1 reply 0 retweets 10 likes
      3. Prof. Anima Anandkumar‏ @AnimaAnandkumar Oct 21
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        Replying to @chenna1985 @kazizzad @Caltech

        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.

        2 replies 0 retweets 20 likes
      4. Show replies
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      2. cameron fen‏ @FenCameron Oct 21
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        Replying to @AnimaAnandkumar @kazizzad @Caltech

        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?

        1 reply 0 retweets 3 likes
      3. cameron fen‏ @FenCameron Oct 21
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        Replying to @FenCameron @AnimaAnandkumar and

        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.

        0 replies 0 retweets 1 like
      4. End of conversation
      1. Ajay Divakaran‏ @ajaydiv Oct 21
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        Replying to @AnimaAnandkumar @kazizzad @Caltech

        Interesting work.

        0 replies 0 retweets 3 likes
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      1. Ulises Moya‏ @UlisesMoya12 Oct 21
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        Replying to @AnimaAnandkumar @kazizzad @Caltech

        Thank you ! We work with CNNs in the Fourier domain

        0 replies 0 retweets 4 likes
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      2. Shobhit Sharma‏ @Shobhit161990 Oct 21
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        Replying to @AnimaAnandkumar @kazizzad @Caltech

        This is actually pretty amazing! Are there any particularly interesting applications you envision for this method?

        1 reply 0 retweets 2 likes
      3. Kamyar Azizzadenesheli‏ @kazizzad Oct 22
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        Replying to @Shobhit161990 @AnimaAnandkumar @Caltech

        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.

        2 replies 0 retweets 8 likes
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