This paper proposes an innovative physics-informed and time-dependent surrogate method based on machine learning to calibrate the parameters of detonation products for cylinder test. Model calibration is a step of model validation, verification, and uncertainty quantification. A good calibration result will effectively enhance the credibility of a simulation, even model and software. This method extracts and quantifies the features of data, and corresponds them to the specific physical processes, such as the fluctuation caused by shock wave and the damping effect caused by energy dissipation. Different from the conventional surrogate models, our method gives a special consideration to the time variable and couples it with the detonation parameters properly through feature extraction and correlation analysis. The use of feature screening and variable selection enables this method to deal with high-dimensional and nonlinear situations. Models based on the Cramer-von Mises conditional statistic can reduce the complexity and improve the generalization performance by screening out the variables with strong correlation. And with the Oracle property of adaptive lasso, the convergence property of the method is guaranteed. Numerical examples of PBX9501 show, that the calibration results effectively improve the accuracy of simulation. With the relation between parameters and feature coefficients, we offer an instructive parameter adjusting strategy. Last but not least it can be generalized to other explosives. Model comparison results on 17 types of explosives show that our method has a better agreement with the cylinder test than the classical exponential form.

中文翻译：

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爆轰产品的模型标定：一种基于机器学习的物理信息依赖于时间的替代方法

本文提出了一种基于机器学习的基于物理的，有时间依赖的创新替代方法，用于校准用于汽缸测试的起爆产品的参数。模型校准是模型验证，验证和不确定性量化的步骤。良好的校准结果将有效地提高仿真，甚至模型和软件的可信度。该方法提取并量化数据的特征，并将其与特定的物理过程相对应，例如冲击波引起的波动和能量耗散引起的阻尼效应。与传统的替代模型不同，我们的方法对时间变量进行了特殊考虑，并通过特征提取和相关分析将其与爆轰参数正确耦合。使用特征筛选和变量选择使该方法能够处理高维和非线性情况。通过筛选具有强相关性的变量，基于Cramer-von Mises条件统计的模型可以降低复杂度并提高泛化性能。利用自适应套索的Oracle属性，保证了该方法的收敛性。PBX9501的数值例子表明，校准结果有效地提高了仿真的准确性。通过参数和特征系数之间的关系，我们提供了一种有启发性的参数调整策略。最后但并非最不重要的一点是，它可以推广到其他炸药。