A Bayesian Framework for Extrapolative Emulation of Spatially Gridded Simulation Data

Abstract

We propose a Bayesian emulator for extrapolating spatially gridded simulation output across resolution. The method treats each pixel as following a nonlinear resolution-response curve, while linking pixels through Gaussian process priors on the curve parameters to preserve spatial structure and quantify uncertainty. In synthetic experiments designed to mimic resolution-dependent bias, the approach recovers the high-resolution target with competitive or improved accuracy relative to several alternative emulators, particularly in lower-information settings, while maintaining near-nominal interpolative coverage. We also illustrate the method on a radiation-hydrodynamics example from Cassio, where it is used to extrapolate a derived wave-front diagnostic beyond the finest observed simulation. These results suggest that spatially regularized resolution extrapolation can provide a useful statistical tool for studying high-fidelity behavior when direct simulation is expensive.