Data-efficient kernel methods learn differential equations and their solution operators; Gauss-Newton optimization improves training efficiency and accuracy.
Read the original at arxiv.org→arXiv:2503.01036v3 Announce Type: replace-cross Abstract: We introduce a novel kernel-based framework for learning differential equations and their solution maps. The framework is efficient in its data requirements...
Original headline: "Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis"
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- Nov 4, 05:00 UTC arXiv cs.LG lead source Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis