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Deep learning for computational structural optimization.

Long C Nguyen1, H Nguyen-Xuan2

  • 1CIRTECH Institute, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam.

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|April 19, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning approach for structural optimization, enhancing computational efficiency and reliability for truss structures. The method shows faster convergence using Chebyshev polynomials as activation functions.

Keywords:
Activation functionChebyshev polynomialDeep learningDeep neural networkOptimizationTruss structures

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional structural optimization methods can be computationally intensive.
  • Deep learning offers potential for accelerating complex engineering computations.

Purpose of the Study:

  • To develop and validate a novel deep learning framework for computational structural optimization.
  • To enhance the efficiency and reliability of structural design processes.

Main Methods:

  • Utilized deep learning algorithms to solve stiffness formulations and optimize structural computations.
  • Employed a constant sum technique for efficient data generation and compared various optimizers (SGD, NAG, RMSProp, Adam).
  • Introduced Chebyshev polynomials as activation functions in single-layer neural networks and proposed a split data technique for linear regression.

Main Results:

  • Demonstrated the reliability of the deep learning approach on 2D and 3D truss benchmark problems.
  • Achieved quicker convergence with Chebyshev polynomial activation functions compared to popular learning functions.
  • The proposed method shows straightforward extension to other engineering structures.

Conclusions:

  • Deep learning provides an effective and efficient approach for structural optimization.
  • Chebyshev polynomials offer a promising alternative for activation functions, improving convergence speed.
  • The framework is adaptable for broader applications in engineering design.