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Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
Using the theory of differential forms to hard-code divergence-free constraints in neural operators
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Efficient Dynamics Modeling in Interactive Environments with Koopman Theory
Using Koopman theory to learn a linear dynamics in a latent space
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Neural operators and chaotic attractors
Training neural operators to preserve invariant measures of chaotic attractors
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Machine Learning and the Physical Sciences
A NeurIPS 2023 workshop
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Physics-informed machine learning meets engineering
An online seminars serie hosted by the Alan Turing Institute