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Friedrich-Alexander-Universität Erlangen-Nürnberg
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Neural PDE solvers can miss optimal solutions due to unbounded parameters, but adding kernel derivatives recovers these "missing limits."
Stiff boundary conditions don't always need full-fidelity simulation: learned error gates determine exactly when a solver can safely drop to cheaper Dirichlet limits without violating numerical tolerances.
