Machine learning applied in the multi-scale 3D stress modelling

• URL: http://arxiv.org/abs/2008.11244v1
• Date: Tue, 25 Aug 2020 19:17:38 GMT
• Title: Machine learning applied in the multi-scale 3D stress modelling
• Authors: Xavier Garcia and Adrian Rodriguez-Herrera
• Abstract summary: This paper proposes a methodology to estimate stress in the subsurface by a hybrid method combining finite element modeling and neural networks. 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. 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.
• Score: 10.355894890759377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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