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Accelerating Multi-Objective Optimization of Composite Structures Using Multi-Fidelity Surrogate Models and

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  • 1Faculty of Civil and Environmental Engineering and Architecture, Rzeszów University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.

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Summary

This study introduces an efficient method for optimizing composite structures using Curriculum Learning (CL) and multi-fidelity surrogate models. The approach reduces computational cost while maintaining accuracy in engineering design.

Keywords:
compositecurriculum learningdeep neural networksgenetic algorithmsmulti-fidelity modelsmulti-objective optimizationsurrogate models

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Area of Science:

  • Engineering
  • Computational Science
  • Materials Science

Background:

  • Optimizing multilayer composite structures involves balancing mechanical performance, cost, and computational demands.
  • Existing methods often face challenges in computational efficiency for complex designs.

Purpose of the Study:

  • To develop an innovative approach for enhancing computational efficiency in engineering design.
  • To integrate Curriculum Learning (CL) with multi-fidelity surrogate models for structural optimization.

Main Methods:

  • Utilized a multi-fidelity strategy with high-fidelity finite element models and low-fidelity models for efficient data generation.
  • Applied Curriculum Learning (CL) to iteratively refine surrogate models for improved accuracy.
  • Employed Genetic Algorithms (GAs) for optimizing structural parameters and minimizing computational expense.

Main Results:

  • Demonstrated a significant reduction in computational burden while preserving prediction accuracy.
  • Validated the methodology by evaluating Pareto front quality using performance indicators.
  • Showcased practical applicability in real-world structural design problems.

Conclusions:

  • The integration of CL, multi-fidelity modeling, and GAs offers an efficient framework for composite structure optimization.
  • This scalable methodology addresses the need for high computational efficiency in complex engineering problems.
  • The study advances optimization techniques for engineering design through innovative computational strategies.