Generative AI for mechanical engineering design and optimization
- Submission status
- Open
- Submission deadline
This Collection supports and amplifies research related to SDG 9 and SDG 12.
The concept of artificial intelligence (AI) as a creative design tool might once have been controversial. But in recent years, generative AI tools have rapidly advanced and are now offering new modes of working and advancing human-AI interaction to explore solution spaces and discover, create and optimise designs.
In this collection we aim to publish exciting advances in the capability of generative AI methods and their application directions within a broad mechanical engineering scope. We are using an inclusive definition of mechanical engineering which can include materials, manufacturing, structural, aerospace and interdisciplinary research. Overarching goals include structural integrity and safety, material and energy efficiency, manufacturability and cost-effectiveness, functionality and performance advances, compliance and regulatory requirements. These goals amplify research related to the United Nations Sustainable Development Goals (SDGs), notably SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Specific research directions might include, but are not limited to, generative or data-driven design, design automation, topology and structure optimization, additive manufacturing, shape and form generation.
We are happy to consider all AI approaches, including multi-modal generative models, physics-informed AI, explainable AI for design, inverse modeling, diffusion models with or without guidance, Large Language Models or Vision Language Models, Transformer-based auto-regressive models and Representation Learning Models amongst others. For researchers working with proprietary large language models such as GPT we highly recommend reproduction of findings with open-source models (such as LLAMA) where all details (models, prompts, scripts etc) can be shared to ensure fair benchmarking with the literature in future works.
Editors
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Cyril Picard, PhD
Massachusetts Institute of Technology, USA
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Kristina Shea, PhD
ETH Zurich, Switzerland
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Binyang Song, PhD
Nanyang Technological University, Singapore
Communications Engineering is edited by both in-house professional editors and Editorial Board Members.
Guest Editors for Communications Engineering
Cyril Picard, PhD, Massachusetts Institute of Technology, USA
Cyril Picard is a Research Scientist within the DeCoDE lab at the Massachusetts Institute of Technology. His research interests include the intersection of artificial intelligence and engineering design, with the development of computational design tools to meet the challenges of modern system engineering. He works on adapting computer science techniques to the requirements of engineering and focuses on developing tools that are readily usable in practice. Prior to joining MIT, he earned a Doctor of Science degree from the École polytechnique fédérale de Lausanne (EPFL) in Switzerland and was a postdoctoral researcher at ETH Zurich.
Kristina Shea, PhD, ETH Zurich, Switzerland
Prof. Kristina Shea is a Full Professor for Engineering Design and Computing in Mechanical and Processing Engineering at ETH Zürich since 2012. She has carried out research in AI in engineering design, specifically generative design, since her PhD work at Carnegie Mellon University in 1997. Her current focus is to develop novel, AI-driven generative design methods that discover new, creative engineering systems.
Binyang Song, PhD, Nanyang Technological University, Singapore
Dr. Binyang Song is an Assistant Professor in the School of Mechanical and Aerospace Engineering at Nanyang Technological University. Her research focuses on applied artificial intelligence and human-AI collaboration in engineering design, with particular interests in data-driven design, generative modeling, and AI-augmented design teams. She has worked extensively on multimodal learning for design evaluation, network analysis for design knowledge extraction, and the integration of AI agents into human-centered design processes. Her work resides at the intersection of engineering design, applied machine learning, and human-computer interaction, aiming to develop generalizable AI-based design methods and advance human-AI teaming in complex system design.
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