AI 中文总结
该研究针对空间网格化模拟数据跨分辨率外推问题，提出贝叶斯模拟器，将像素视为遵循非线性曲线并通过高斯过程先验连接，在合成实验及辐射流体动力学示例中表现良好，可为高保真行为研究提供统计工具。
AI 中文摘要
我们提出了一种贝叶斯模拟器，用于跨分辨率外推空间网格化模拟输出。该方法将每个像素视为遵循非线性分辨率响应曲线，同时通过曲线参数上的高斯过程先验连接像素，以保留空间结构并量化不确定性。在旨在模拟分辨率相关偏差的合成实验中，该方法相对于其他几种替代模拟器，以具有竞争力或更高的精度恢复高分辨率目标，特别是在低信息设置中，同时保持接近标称的插值覆盖率。我们还在卡西奥的辐射流体动力学示例中说明了该方法，该方法用于将导出的波前诊断外推到最精细观测模拟之外。这些结果表明，当直接模拟成本高昂时，空间正则化分辨率外推可以为研究高保真行为提供有用的统计工具。
英文摘要
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.
