Equation Recast for Canonical Operator Learning Across Parametric PDEs
Equation recast is introduced, which reformulates parametric operator learning as the learning of a single canonical operator, and provides a route toward reusable neural PDE solvers combining equation-guided transfer, data efficiency, and monitorable inference.
Qi-Yun Cheng, Valentin Duruisseaux, C. Clauser et al.
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