Special Issue on Scientific Machine Learning for Manufacturing Processes and Material Systems

Journal of Computing and Information Science in Engineering

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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. 

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Topic Areas

THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:

Physics-informed ML for process/materials design and optimization Physics-informed ML for diagnostics, prognostics, and process control Uncertainty quantification in scientific machine learning Leveraging high-throughput framework for modeling and optimization Efficient modeling through adaptive and active learning algorithms

Explainable AI and causal inference augmented predictive modeling Exploring state-of-the-art ML algorithms in modeling and optimization Understanding of systems through knowledge representation and reasoning Leveraging data-fusion and multi-fidelity techniques in modeling