预测不确定性可控的计算机模型校准方法提出，区间宽度约为 KOH 的 20%
Prediction Uncertainty Controlled Computer Model Calibration
教 AI 学校准仿真参数的新招，区间比 KOH 窄 80% 还能保证覆盖率，做流行病建模的可以看看。
一篇 arXiv 论文提出两层校准框架，直接控制校准后模拟器的预测误差。第一层用条件 flow-matching 模型学习校准参数的摊销后验，训练数据是经不同结构高斯过程扰动得到的模拟器输出。第二层用 conformal risk control 从后验样本中选出满足预设时间平均误覆盖率的候选集。在合成基准上，该方法的区间宽度约为 KOH-MCMC 的 20%，误覆盖率接近 10% 目标；在用年龄结构 SEIR 模型拟合 2010 年马拉维麻疹疫情时，未覆盖的周次集中在疫情不对称下降阶段，暴露出机理模型的结构性缺陷。
Prediction Uncertainty Controlled Computer Model Calibration
Computer-model calibration is usually undertaken in service of prediction, yet neither Bayesian Kennedy-O'Hagan (KOH) calibration nor amortized simulation-based inference is built to control the predictive error of the calibrated simulator. When parameters are weakly identified or the data-model discrepancy is misspecified, the simulator-output bands induced by a nominal posterior level can be far from nominal. We propose a two-layer framework that targets predictive miscoverage directly. First, a conditional flow-matching model learns an amortized posterior for the calibration parameters, trained on simulator outputs perturbed with Gaussian-process discrepancies of varying structure. Second, conformal risk control arranges posterior draws for the target observation into nested candidate sets and selects the smallest one whose fixed-rank pointwise bands attain a prescribed time-averaged miscoverage on a synthetic calibration sample. Under exchangeability of calibration and target observations, the bands control expected miscoverage in finite samples. In a synthetic benchmark, the proposed bands are roughly 20% the width of KOH-MCMC bands with miscoverage near the 10% target. In calibrating an age-structured SEIR model to the 2010 Malawi measles outbreak, they are again sharper and near-nominal, and the weeks they fail to cover coincide with the asymmetric epidemic decline, exposing structural inadequacy of the mechanistic model.
