In regimes of high strain rate, the strength of materials often cannot be measured directly in experiments. Instead, the strength is inferred based on an experimental observable, such as a change in shape, that is matched by simulations supported by a known strength model. In hole closure experiments, the rate and degree to which a central hole in a plate of material closes during a dynamic loading event are used to infer material strength parameters. Due to the complexity of the experiment, many computationally expensive, three-dimensional simulations are necessary to train an emulator for calibration or other analyses. These simulations can be run at multiple grid resolutions, where dense grids are slower but more accurate. In an effort to reduce the computational cost, a combination of simulations with different resolutions can be combined to develop an accurate emulator within a limited training time. We explore the novel design and construction of an appropriate functional recursive multi-fidelity emulator of a strength model for tantalum in hole closure experiments that can be applied to arbitrarily large training data. Hence, by formulating a multi-fidelity model to employ low-fidelity simulations, we were able to reduce the error of our emulator by approximately 81% with only an approximately 1.6% increase in computing resource utilization.

中文翻译：

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用于模拟孔闭合实验的递归多保真模型的实际扩展

在高应变率的情况下，材料的强度通常无法在实验中直接测量。取而代之的是，强度是基于实验观察到的，例如形状的变化，与已知强度模型支持的模拟相匹配。在孔闭合实验中，材料板中心孔在动态加载事件期间闭合的速率和程度用于推断材料强度参数。由于实验的复杂性，需要许多计算成本高的三维模拟来训练模拟器以进行校准或其他分析。这些模拟可以在多个网格分辨率下运行，其中密集网格速度较慢但更准确。为了降低计算成本，可以结合不同分辨率的模拟组合，在有限的训练时间内开发准确的模拟器。我们在孔闭合实验中探索了钽强度模型的适当功能递归多保真仿真器的新颖设计和构造，该仿真器可应用于任意大的训练数据。因此，通过制定多保真模型以采用低保真模拟，我们能够将仿真器的错误降低约 81%，而计算资源利用率仅增加约 1.6%。我们在孔闭合实验中探索了钽强度模型的适当功能递归多保真仿真器的新颖设计和构造，该仿真器可应用于任意大的训练数据。因此，通过制定多保真模型来采用低保真模拟，我们能够将仿真器的错误降低约 81%，而计算资源利用率仅增加约 1.6%。我们在孔闭合实验中探索了钽强度模型的适当功能递归多保真仿真器的新颖设计和构造，该仿真器可应用于任意大的训练数据。因此，通过制定多保真模型来采用低保真模拟，我们能够将仿真器的错误降低约 81%，而计算资源利用率仅增加约 1.6%。