CHOP reduces relative inference error on OOD operator tasks for scalar conservation laws and mean-field control by composing frozen ICON with explicit closed-form elementary operators that remain interpretable.
Lemon: Learning to learn multi-operator networks.arXiv preprint arXiv:2408.16168, 2024
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DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.
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Harness In-Context Operator Learning with Chain of Operators
CHOP reduces relative inference error on OOD operator tasks for scalar conservation laws and mean-field control by composing frozen ICON with explicit closed-form elementary operators that remain interpretable.
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Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.