用贝叶斯逆不确定性量化改进核数据调整，提升非线性应用精度。
Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation
- 基于贝叶斯逆不确定性量化（IUQ）进行核数据调整，直接利用计算模型响应。
- 在非线性场景下，传统方法（GLLS）预测失效，IUQ与MOCABA结果更接近真实值。
- 低相关性实验仍具信息量，可指导未来实验选择，适合核安全研究者参考。
经合组织（OECD）核临界安全工作组（WPNCS）发起基准测试，评估现有核数据调整技术在非线性应用及与应用相关性低的实验中的表现。本文引入贝叶斯逆不确定性量化（IUQ）方法，对比传统广义线性最小二乘法（GLLS）和蒙特卡洛贝叶斯法（MOCABA）。对线性应用，IUQ后验预测与GLLS、MOCABA一致；在非线性应用中，GLLS无法复现计算响应分布，MOCABA接近真实，而IUQ直接使用计算模型响应。研究还发现，与应用相关性低的实验仍具信息价值，并识别出有助于筛选调整实验的关键特性。该基准测试表明，贝叶斯IUQ在核数据调整中具有潜力。
原文摘要 · Abstract (English)
The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. When comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that GLLS predictions fail to replicate computed response distributions for nonlinear applications, while MOCABA shows near agreement, and IUQ uses computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.
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