论文标题
多元机器学习预测的不确定性界限
Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture
论文作者
论文摘要
在脆性材料中进行的高应变率影响实验的裂纹网络演变模拟非常密集。如果需要多次模拟来说明裂纹长度，位置和方向的随机性，那么成本将增加，这是在现实世界中固有发现的。构建机器学习模拟器可以通过数量级更快地使过程更快。但是，几乎没有工作评估与他们的预测相关的错误。估计这些错误对于有意义的总体不确定性定量至关重要。在这项工作中，我们将异质不确定性估计值扩展到绑定多重输出机学习模拟器。我们发现响应预测在其预测错误中是准确的，但对不确定性的保守估计有些保守。
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.
