Calibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert knowledge. We propose a sensitivity-guided iterative parameter identification and data generation algorithm. The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the physics-enhanced latent space variational autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. A complete calibration experiment was conducted on a thermal model of an automotive cabin. The average relative error rate over all parameter estimates of 1.62% and the mean absolute error of calibrated model outputs of $$0.108\,^{\circ }$$ C validate the feasibility and effectiveness of the method. Moreover, the entire procedure accelerates up to 1 day, whereas the classical calibration method takes more than 1 week.

中文翻译：

---

使用 BayesFlow 和 PELS-VAE 进行灵敏度引导的迭代参数识别和数据生成，以进行模型校准

由于依赖包罗万象的专家知识，使用标准优化方法对具有大量参数的复杂系统模型进行校准通常非常耗时，并且不能完全自动化。我们提出了一种灵敏度引导的迭代参数识别和数据生成算法。灵敏度分析取代了人工干预，参数识别由 BayesFlow 实现，允许不确定性量化，并且在两个迭代步骤之间使用物理增强的潜在空间变分自动编码器 (PELS-VAE) 生成数据，可以推断弱可识别参数。在汽车驾驶室的热模型上进行了完整的校准实验。所有参数估计值的平均相对误差率为 1。62%和校准模型输出的平均绝对误差$$0.108\,^{\circ }$$ C验证了该方法的可行性和有效性。此外，整个过程最多可加快 1 天，而经典校准方法则需要 1 周以上。