Spatially defined optimization of FEA using nodal surrogate models

Thelin, Christopher; Bunnell, Spencer; Gorrell, Steven E.; Wright, Landon; Salmon, John L.

doi:10.1007/s00158-021-02894-3

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…To accelerate the design space exploration, many researchers focused on building surrogate models for FEA to establish the relationship between design parameters and responses. [2][3][4][5] Wang et al [3] proposed a convolutional neural network to establish a relationship between the material parameters and its peak stress. Although this model achieves high accuracy of 98.79%, the feasibility of developing a stress analysis agent model using a deep learning approach cannot be verified due to the simple model setup and single target task.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Analysis for Structure Design via Deep Learning Surrogate Models

Shao

Chen

Wang

et al. 2023

Advanced Intelligent Systems

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Using computer simulation tools such as finite element analysis (FEA) to perform material stress analysis is a common design method in engineering practice. In order to model more realistic real‐world systems, simulation models have become more complex, and calculation becomes more expensive as a result. The rise of artificial intelligence technologies has made it possible to integrate deep learning methods and material stress analysis. Herein, FEA software is employed to obtain a large number of analysis cases as training samples and uses a fully connected neural network and long‐short‐term memory neural network as surrogate models, which can predict the stress distribution and stress sequence in the process of the bullet impacting target plates with different materials. These models can give results similar to FEA with 92.19% and 92.41% accuracy, respectively. The experimental results show that the deep learning surrogate models have great potential in material stress analysis.

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