Special issue: scientific machine learning for manufacturing processes and material systems

Michopoulos, John G. ; Bhaduri, Anindya ; Chinesta, Francisco ; Cueto, Elías (Universidad de Zaragoza) ; Liu, Dehao ; Ravi, Sandipp Krishnan ; Wang, Jian-Xun

Resumen: Computational modeling, simulation, and optimization of manufacturing processes and materials systems have been a persistent endeavor of the engineering research community at large. Significant progress has been achieved in this field due to the exponential increase in computing power, and the incorporation of data-driven modeling methods. Process and systems modeling often involves expensive and time-intensive simulations and experiments. Incorporation of machine-learning (ML) models as efficient surrogate models has been proven to enhance the human understanding of the behavior of the system at hand and reduce the computational 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 presence of noise in data, limited budget, data sparsity, lack of interpretability of ML models, etc. Tackling these issues will enable more comprehensive modeling of manufacturing processes and discovery of novel material systems.

Idioma: Inglés

DOI: 10.1115/1.4066791

Año: 2024

Publicado en: JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 24, 11 (2024), 110301 [3 p.]
ISSN: 1530-9827

Factor impacto JCR: 3.3 (2024)

Categ. JCR: ENGINEERING, MANUFACTURING rank: 33 / 71 = 0.465 (2024) - Q2 - T2
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 76 / 175 = 0.434 (2024) - Q2 - T2

Factor impacto SCIMAGO: 0.788 - Industrial and Manufacturing Engineering (Q1) - Computer Graphics and Computer-Aided Design (Q1) - Software (Q2) - Computer Science Applications (Q2)

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)