发表机构
Rochester Institute of Technology; Los Alamos National Laboratory（罗切斯特理工学院; 洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将Fisher信息与可微求解器结合，通过局部高斯近似快速量化逆问题中潜在场及其预测的不确定性，应用于核光学势时，改善了截面预测并实现了更窄的置信区间。
AI 中文摘要
物理学中的许多逆问题通过控制方程从测量数据推断未观测的场。我们将Fisher信息与可微求解器相结合，利用局部高斯近似量化推断场及其预测可观测量的不确定性，无需对完整参数后验进行采样。应用于核光学势时，该方法能够区分由散射测量约束的特征与不确定性仍由先验设定的特征。我们的灵活模型改善了与留出截面的一致性，并在比既有贝叶斯光学模型更窄的预测区间内实现了接近名义覆盖率。
英文摘要
Many inverse problems in physics infer an unobserved field from measurements connected to it through a governing equation. We combine Fisher information with a differentiable solver to quantify uncertainty in both the inferred field and its predicted observables, using a local Gaussian approximation without sampling the full parameter posterior. Applied to nuclear optical potentials, the method distinguishes features constrained by scattering measurements from those whose uncertainty remains set by the prior. Our flexible model improves agreement with held-out cross sections and achieves nearly nominal coverage with narrower predictive intervals than an established Bayesian optical model.
Commentsv3: expanded AI use disclosure in Acknowledgements
