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The impact of AI on engineering design procedures for dynamical systems

arXiv 2412.12230 ↗First posted 16 Dec 2024eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program SPP~2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 208 references that are in the map. First posted on arXiv 16 Dec 2024.

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References · 12

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
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  3. 2013Auto-Encoding Variational Bayescited by 4,053
  4. 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 872
  5. 2020The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policiescited by 75
  6. 2019Learning Discrepancy Models From Experimental Datacited by 15
  7. 2024What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANscited by 12
  8. 2020From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learningcited by 7
  9. 2023Learning to Predict Structural Vibrationscited by 4
  10. 2024Solving Partial Differential Equations with Equivariant Extreme Learning Machinescited by 2
  11. 2024Reconstruction of continuum robots by marker-free shape registration of image data using a kinematic modelcited by 2
  12. 2024Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditionscited by 1

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