Congratulations to @caltech undergrads on winning the Goldwater scholarship including Miguel who has been making key contributions to neural operators
@animaanandkumar
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Mentor Recognition for TED Talks Speaking Opportunity
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Thank you @SCR10 for being an amazing mentor for my @TEDTalks
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Building AI with Universal Physical Understanding
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Looking forward to my @TEDTalks on building #AI with universal physical understanding. Excited to announce our recent works building the foundations for such a model. Language models have shown impressive capabilities with universal text understanding capabilities, but they are
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AI and Science Talk at TED Vancouver Conference
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Vancouver and @TEDTalks here I come! So psyched to be speaking on AI + Science!
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AI and Science: Raising Awareness on Critical Intersection
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It was a pleasure to talk to @prateekvjoshi about AI+Science and increase awareness around this important area! https://t.co/f2gxFxI0LA
— Prof. Anima Anandkumar (@AnimaAnandkumar) 10 avril 2024It was a pleasure to talk to @prateekj about AI+Science and increase awareness around this important area!
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Neural Operators as Generalization of Neural Radiance Fields
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How does neural operators relate to neural radiance fields (NeRF)? Neural operators are a generalization of NeRFs: from representing a single function to learning operators. Neural operators are conditional neural fields, conditioned on different input functions. Our neural
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Neural Operators as Generalizations of PDE Numerical Solvers
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Neural operators are extensions of neural networks to handle continuous inputs and outputs (function spaces) and hence, accurately represent solutions to PDEs. But how do they relate to numerical solver for PDEs? Figure below shows the pseudo-spectral solver is a special case of
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Neural Operators Capture Finer Scales Than Traditional Networks
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The advantage of neural operators over neural networks (e.g. UNet or transformer) is in the ability to capture finer scales than the observed data resolution. In left figure below, the prediction at higher wave number (resolution) is where neural networks (NN) suffer if
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Neural Operators Generalize Neural Networks Beyond Fixed Dimensions
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The class of neural operators contain all neural networks. Neural networks fix resolution or dimension of input and outputs before learning the mapping, while in neural operators we do not have this restriction. In figure, you can see this difference: neural networks assumes
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Neural operators accelerate simulations and design
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Our @NatRevPhys perspective article on neural operators and their ability to accelerate simulations and design is now out. https://
rdcu.be/dD8BI @Nature 1. Neural operators learn mappings between functions, e.g. spatiotemporal processes and partial differential equations.