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arXiv · 2607.20703

A Bayesian Framework for Extrapolative Emulation of Spatially Gridded Simulation Data

Kelly R. Moran·Ky Potter·Chris Danly·Christopher L. Fryer
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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.

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Kelly R. Moran, Ky Potter, Chris Danly, Christopher L. Fryer. 2026-07-22. A Bayesian Framework for Extrapolative Emulation of Spatially Gridded Simulation Data. https://arxiv.org/abs/2607.20703

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