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Hyperparameter Selection via Early Stopping for Bayesian Semilinear PDEs
We provide a data-driven method for choosing the scale of a Gaussian prior for non-linear Bayesian inverse problems arising from semilinear PDEs. Following \cite{koers2024}, the non-linear model for the parameter $f$ is reparametrized as a linear model for $v = \mathbb{L}u_f$, in which the prior sca
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