论文标题
在多尺度3D应力建模中应用的机器学习
Machine learning applied in the multi-scale 3D stress modelling
论文作者
论文摘要
本文提出了一种通过结合有限元建模和神经网络的混合方法来估计地下压力的方法。该方法利用了在行为涉及较大长度尺度的系统的数值建模中获得多频解决方案的想法。通过粗略的规模廉价有限元建模获得了一种低频解决方案。第二个解决方案提供了由自由参数的异质性以精细比例引入的细粒细节。通过通过高分辨率有限元模型获得的部分溶液训练的神经网络估算了这种高频溶液。当粗糙有限元元素解决方案与神经网络估计相结合时，结果在结果的2 \％误差范围内，该误差将使用高分辨率有限元模型计算。本文讨论了该方法的好处和缺点，并通过工作示例说明了其适用性。
This paper proposes a methodology to estimate stress in the subsurface by a hybrid method combining finite element modeling and neural networks. This methodology exploits the idea of obtaining a multi-frequency solution in the numerical modeling of systems whose behavior involves a wide span of length scales. One low-frequency solution is obtained via inexpensive finite element modeling at a coarse scale. The second solution provides the fine-grained details introduced by the heterogeneity of the free parameters at the fine scale. This high-frequency solution is estimated via neural networks -trained with partial solutions obtained in high-resolution finite-element models. When the coarse finite element solutions are combined with the neural network estimates, the results are within a 2\% error of the results that would be computed with high-resolution finite element models. This paper discusses the benefits and drawbacks of the method and illustrates their applicability via a worked example.
