چکیده مقاله
Artificial intelligence AI has transformed computational modeling across diverse industrial sectors Among AI approaches, Deep Neural Networks DNNs , Physics Informed Neural Networks PINNs , Graph Neural Networks GNNs , and generative models demonstrate distinct strengths in solving complex engineering and scientific problems This review systematically examines these methodologies, comparing their architectures, accuracy, computational efficiency, and applicability in real world scenarios Key findings reveal that while DNNs excel in general pattern recognition, PINNs effectively incorporate physical constraints, GNNs efficiently handle structured relational data, and generative models enable novel design and prediction tasks The study further identifies current limitations, including scalability, interpretability, and integration challenges, and discusses strategies to overcome them By providing a comprehensive evaluation, this work offers critical insights for researchers and practitioners seeking to select or develop AI methods tailored to industrial applications The review also outlines promising future directions, emphasizing hybrid models and explainable AI to enhance performance and adoption
کلیدواژهها
نویسندگان
Behnam Hatami
۱ Lecturer, Department of Engineering, National Skill based University of Iran, Eslamabad e Gharb, Iran
Reza Rashidi
۲ Lecturer, Department of Engineering, National Skill based University of Iran, Eslamabad e Gharb, Iran
شیوه ارجاع
Hatami, Behnam and Rashidi, Reza,1404,A Review on Data-Driven and Deep Learning Methods in Computational Solid Mechanics,28th National Conference on Electrical, Computer and Mechanical Engineering,Shirvan
ارائهشده در
مجموعه مقالات بیست و هشتمین کنفرانس ملی مهندسی برق، کامپیوتر و مکانیک25 آذر 1404 · شیروان