AI-Driven Engineering Optimization

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) is transforming the landscape of engineering design, analysis, and optimization. However, this evolution is accompanied by increasing complex challenges. Modern engineering systems are characterized by intricate interdependencies, vast data streams, and a constant need to adapt to dynamic environments. Traditional engineering optimizations struggle to address those challenges with need for real-time optimal decision-making, the inherent uncertainties of complex systems, and large scale of data. 

From generative design algorithms to physics-informed neural networks, AI-driven methods are unlocking unprecedented opportunities to solve complex, multi-disciplinary engineering challenge.  The intersection and combination of artificial intelligence and optimization will present new ways for developing innovative engineering solutions. AI has a strong capacity for pattern recognition, and decision analysis, while the strengths of engineering optimization are optimization, simulation and decision-making of vast engineering problems. Their combination will help us to develop next generation optimization methods that are not only efficient and effective, but also robust, adaptable and capable of exploring the complexities of the interconnected world.    

This Special Issue invites researchers, practitioners and engineers to share work at the intersection of AI, machine learning and engineering optimization to explore cutting-edge advancements, practical applications and future directions in AI-driven optimization across engineering domains, topics of interest include but not limited to:

• AI/ML techniques for engineering optimization
  • AI techniques: Neural networks, machine learning.
  • Evolutionary computation: including genetic algorithms, particle swarm optimization, ant colony optimization and differential evolution etc.
  • Hybrid models combining AI with traditional numerical methods.
  • Generative /Multimodal optimization: explore new solutions.
  • Design automation.
  • Data-driven optimization.

• Applications across Engineering Disciplines
  • Aerospace, automotives, civil, mechanical, chemical and energy systems etc.
  • Real-time optimization for robotics and smart engineering design.
  • Large scale engineering optimization.
  • AI applications in engineering optimization.

Dr. Jian-Ping Li
Dr. Mudhar Al-Obaidi
Dr. Qingsheng Li
Guest Editors

Manuscript Submission Information

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