000147247 001__ 147247
000147247 005__ 20250923084434.0
000147247 0247_ $$2doi$$a10.1115/1.4066791
000147247 0248_ $$2sideral$$a141080
000147247 037__ $$aART-2024-141080
000147247 041__ $$aeng
000147247 100__ $$aMichopoulos, John G.
000147247 245__ $$aSpecial issue: scientific machine learning for manufacturing processes and material systems
000147247 260__ $$c2024
000147247 5060_ $$aAccess copy available to the general public$$fUnrestricted
000147247 5203_ $$aComputational 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.
000147247 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000147247 590__ $$a3.3$$b2024
000147247 592__ $$a0.788$$b2024
000147247 591__ $$aENGINEERING, MANUFACTURING$$b33 / 71 = 0.465$$c2024$$dQ2$$eT2
000147247 593__ $$aIndustrial and Manufacturing Engineering$$c2024$$dQ1
000147247 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b76 / 175 = 0.434$$c2024$$dQ2$$eT2
000147247 593__ $$aComputer Graphics and Computer-Aided Design$$c2024$$dQ1
000147247 593__ $$aSoftware$$c2024$$dQ2
000147247 593__ $$aComputer Science Applications$$c2024$$dQ2
000147247 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000147247 700__ $$aBhaduri, Anindya
000147247 700__ $$aChinesta, Francisco
000147247 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza
000147247 700__ $$aLiu, Dehao
000147247 700__ $$aRavi, Sandipp Krishnan
000147247 700__ $$aWang, Jian-Xun
000147247 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000147247 773__ $$g24, 11 (2024), 110301 [3 p.]$$pJ. Comput. Inf. Sci. Eng.$$tJOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING$$x1530-9827
000147247 8564_ $$s126905$$uhttps://zaguan.unizar.es/record/147247/files/texto_completo.pdf$$yPostprint
000147247 8564_ $$s2404179$$uhttps://zaguan.unizar.es/record/147247/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000147247 909CO $$ooai:zaguan.unizar.es:147247$$particulos$$pdriver
000147247 951__ $$a2025-09-22-14:45:35
000147247 980__ $$aARTICLE