Special Issue on Generative AI for Design, Manufacturing Processes, and Materials Systems
Journal of Computing and Information Science in Engineering
This special issue aims to collect contributions that address the challenges of generative AI in design, manufacturing processes, and material systems, while exploring its potential across different domains and applications.
Topic Areas
THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:
- Application and standardization of LLMs and VLMs for the engineering domain
- Leveraging generative and inverse modeling frameworks (GANs, VAEs, invertible neural network, flow-based models, diffusion models, etc.) for design and optimization
- Topology optimization driven through generative modeling methods
- Extending understanding through knowledge curation, extraction, and representation
- Data fusion and multi-modal modeling in manufacturing process modeling, monitoring, control, and optimization
- Generative AI for temporal data in engineering systems
- Augmentation of generative modeling through Explainable AI frameworks
- Leveraging multimodal data for generative modeling in material systems
- Studying the effects of generative AI in stimulating creativity in design
- Enhancing the trustworthiness and reliability of generative AI in engineering applications
Submission Instructions
Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here.Once at the Paper Submittal page, select the Journal of Computing and Information Science in Engineering, and then select the Special Issue on Generative AI for Design, Manufacturing Processes, and Materials Systems.
Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.
Guest Editors
Wei Wayne Chen, Texas A&M University, USA (w.chen@tamu.edu)
Vinayak Raman Krishnamurthy, Texas A&M University, USA (vinayak@tamu.edu)
Yanglong Lu, Hong Kong University of Science and Technology, Hong Kong (maeylu@ust.hk)
Jianxi Luo, City University of Hong Kong, Hong Kong (jianxi.luo@cityu.edu.hk)
Chris McComb, Carnegie Mellon University, USA (ccm@cmu.edu)
Sandipp Krishnan Ravi, GE Aerospace Research, USA (sandippkrishnan.ravi@ge.com)
Zhenghui Sha, University of Texas at Austin, USA (zsha@austin.utexas.edu)