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arXiv 2012.15739cond-mat.mtrl-scics.LG
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高应变脆性断裂多变量机器学习预测的不确定性界

Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture

Los Alamos National Laboratory(洛斯阿拉莫斯国家实验室);Lawrence Livermore National Laboratory(劳伦斯利弗莫尔国家实验室)

Cristina Garcia-Cardona, M. Giselle Fernández-Godino, Daniel O'Malley, Tanmoy Bhattacharya

摘要:

脆性材料高应变率冲击实验中裂纹网络演化的模拟计算量很大。如果需要多次模拟来考虑裂纹长度、位置和方向的随机性,则成本会增加得更多,这在现实世界的材料中是固有的。构建一个机器学习仿真器可以使这个过程快上几个数量级。然而,在评估与他们的预测相关的误差方面,几乎没有工作。估计这些误差对于有意义的总体不确定性量化是必要的。在这项工作中,我们将异方差不确定性估计扩展到多输出机器学习仿真器。我们发现,响应预测是准确的预测误差范围内,但有点保守估计的不确定性。

英文摘要:

Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive. The cost increases even more if multiple simulations are needed to account for the randomness in crack length, location, and orientation, which is inherently found in real-world materials. Constructing a machine learning emulator can make the process faster by orders of magnitude. There has been little work, however, on assessing the error associated with their predictions. Estimating these errors is imperative for meaningful overall uncertainty quantification. In this work, we extend the heteroscedastic uncertainty estimates to bound a multiple output machine learning emulator. We find that the response prediction is accurate within its predicted errors, but with a somewhat conservative estimate of uncertainty.

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