A block-diagonal symmetrizer and algebraic conditions on closure blocks enable a data-learnable parametrization of ML moment closures for 2D RTE that guarantees symmetrizable hyperbolicity by construction.
Solving high-dimensional partial differential equations using deep learning.Proceedings of the National Academy of Sciences, 115(34):8505– 8510
2 Pith papers cite this work. Polarity classification is still indexing.
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GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.
citing papers explorer
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Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensions
A block-diagonal symmetrizer and algebraic conditions on closure blocks enable a data-learnable parametrization of ML moment closures for 2D RTE that guarantees symmetrizable hyperbolicity by construction.
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Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.