SOURCE-LINKED INTELLIGENCE
Equation Recast for Canonical Operator Learning Across Parametric PDEs
Learning solution operators across broad parameter ranges can require substantial coverage of both input functions and physical parameters, particularly for purely data-driven parametric models. In addition, the resulting models may fail silently outside the training distribution. We introduce equation recast, which reformulates parametric operator learning as the learning of a single canonical operator. Parameter-induced operator variations are derived analytically from the governing equation and absorbed into effective sources, enabling zero-shot prediction across new parameter regimes. Acro
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T13:15:50.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.