发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对材料表征中基于图像的逆问题，提出SBOCF方法，通过优化复合函数实现高效参数估算，在基准测试和实验数据上表现优于标准贝叶斯优化，可用于下游成像重建。
AI 中文摘要
从科学图像中估算物理参数是材料表征中常见的逆问题，通常依赖于计算成本高昂的基于物理的模拟。在电子显微镜中，样品厚度和晶体失配角是决定电子如何穿透样品散射的关键参数，因此也决定了从样品中重建的任何原子级结构的准确性。人们通常通过将实验性的位置平均会聚束电子衍射（PACBED）图案与模拟图案匹配来推断这些参数，但网格搜索的扩展性较差，而神经网络方法需要大量预训练，可能无法迁移到新的条件下。在此，我们提出了复合函数可扩展贝叶斯优化（SBOCF），这是一种模拟高效的方法，它利用了图像匹配目标的已知复合结构以及模拟图像中包含的中间信息。通过用补丁级摘要和两个校正项表示PACBED图像，SBOCF保留了原始逐像素目标，同时将建模输出的数量从24649减少到11。在50次模拟器评估的预算下，SBOCF在合成SrTiO₃基准测试（包含厚样品和薄样品）上的表现优于带有预期改进的标准贝叶斯优化，在厚样品的情况下，将最终的中值SSE降低了多达290倍。在实验数据上，SBOCF在没有特定任务预训练的情况下产生了与先前报道的值一致的参数估计。对于模拟的失配角样品，在下游的叠层成像重建中使用SBOCF的估计值，重建出了原本模糊的清晰原子。这些结果表明，SBOCF是一种用于涉及昂贵模拟器和高维结构化输出的逆问题的有前景的方法。
英文摘要
Estimating physical parameters from scientific images is a common inverse problem in materials characterization that often relies on expensive physics-based simulations. In electron microscopy, specimen thickness and crystal mistilt are critical parameters that govern how electrons scatter through the sample, and therefore the accuracy of any atomic-scale structure recovered from it. They are commonly inferred by matching experimental position-averaged convergent-beam electron diffraction (PACBED) patterns to simulated ones, but grid searches scale poorly and neural-network methods require extensive pretraining that may not transfer to new conditions. Here, we propose scalable Bayesian optimization of composite functions (SBOCF), a simulation-efficient method that exploits the known composite structure of the image-matching objective and the intermediate information contained in simulated images. By representing PACBED images with patch-level summaries and two correction terms, SBOCF preserves the original pixel-wise objective while reducing the number of modeled outputs from 24,649 to 11. Under a budget of 50 simulator evaluations, SBOCF outperformed standard Bayesian optimization with expected improvement on synthetic SrTiO3 benchmarks with thick and thin specimens, reducing the median final SSE by up to 290x in the thick-sample case. On experimental data, SBOCF produced parameter estimates consistent with previously reported values without task-specific pretraining. For a simulated mistilted specimen, using the SBOCF estimates in a downstream ptychographic reconstruction recovered sharp atoms that were otherwise blurred. These results establish SBOCF as a promising approach for inverse problems involving expensive simulators and high-dimensional structured outputs.
Comments28 pages. Dasol Yoon and Poompol Buathong contributed equally
