Special Issue on Scientific Machine Learning for Manufacturing Processes and Material Systems
Journal of Computing and Information Science in EngineeringSubmit Paper
Computational modeling, simulation, and optimization of manufacturing processes and materials systems have been a persistent endeavor of the engineering research community at large. The two primary factors that achieved significant progress in this field are exponential increases in computing power and the incorporation of data-driven modeling methods. Process and systems modeling often involve expensive and time-intensive simulations and experiments, but incorporation of machine learning (ML) models as efficient surrogate models could potentially enhance the understanding and reduce the optimization cost of the concerned processes and systems.
However, there is a rising need to go beyond the conventional data-driven techniques to address challenges, such as the presence of noise in data, limited budget, data sparsity, and lack of interpretability of ML models. Tackling these issues will enable more comprehensive modeling of manufacturing processes and discovery of novel material systems. From this, the new paradigm of Scientific Machine Learning is emerging, seeking to incorporate domain-awareness, interpretability, and robustness into the models and modeling techniques.
THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:
Submission InstructionsPapers 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 ASME Journal of Computing and Information Science in Engineering, and then select the Special Issue on Scientific Machine Learning for Manufacturing Processes and Material Systems.
Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.