arXiv cs.AI· Exauc\'e Luweh Adjim Ngarti (AIRSEA), \'Elise Arnaud (AIRSEA), L\'eo Nicoletti (Mines Saint-\'Etienne MSE, LIMOS, FAYOL-ENSMSE, FAYOL-ENSMSE), Victor Trappler (Mines Saint-\'Etienne MSE, LIMOS, FAYOL-ENSMSE, FAYOL-ENSMSE), Arthur Vidard (AIRSEA)·· 2 小时前AI 评分27
局部化摊销后验推断用于模型校准：以海洋湍流闭合为例
Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure
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研究者提出一种局部化摊销后验推断方法，通过按观测与目标上下文的接近度加权似然来构建局部化后验族，并用条件归一化流实现摊销变分推断。在 Branin-Hoo 孪生实验中，局部化推断相比全局后验降低了总体预测误差。将该方法应用于单柱海洋模型的 k-ε 湍流闭合（以大气表面强迫为上下文），局部化后验校准相比确定性和全局后验校准减少了温度与纬向流速误差。
正文 · 原文
Abstract:Model discrepancy may prevent a single calibration parameter from reproducing a physical response across the context domain. In addition, uncertainties in observations and contexts affect calibration and must be accounted for and quantified. We construct a family of localized posteriors by weighting observation likelihoods according to their proximity to a target context. Gaussian localization weights incorporate context uncertainty through covariance inflation, with reflection at domain boundaries and an optional correction for uneven context sampling. A conditional normalizing flow approximates the family of posteriors through amortized variational inference. Integrating the localized posteriors under a prescribed context measure gives the marginalized parameter distribution. In a twin experiment with the Branin-Hoo model, we compare global and localized inference under uniform and skewed context distributions. Localization reduces the reported aggregate predictive errors relative to the global posterior. An application to the k-$\epsilon$ turbulence closure in a single-column ocean model, with atmospheric surface forcing as context, shows that localized posterior calibration reduces temperature and zonal-velocity errors relative to deterministic and global-posterior calibration. Both twin experiments are constructed to admit local compensation.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.37731 [cs.AI] |
| (or arXiv:2610.37731v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37731 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Exauce LUWEH ADJIM NGARTI [view email] [via CCSD proxy]
[v1]
Tue, 27 Oct 2026 12:50:10 UTC (3,312 KB)
来源：arXiv cs.AI · arxiv.org