Error bounds for PDE-regularized learning

14 Mar 2020Carsten GräserPrem Anand Alathur Srinivasan

In this work we consider the regularization of a supervised learning problem by partial differential equations (PDEs) and derive error bounds for the obtained approximation in terms of a PDE error term and a data error term. Assuming that the target function satisfies an unknown PDE, the PDE error term quantifies how well this PDE is approximated by the auxiliary PDE used for regularization... (read more)

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