... the model to weigh the importance of different segments within the input text sequence [ 9 ]. Their introduction has been a breakthrough in artificial intelligence (AI) and their capabilities make them well-suited for key NLP tasks including IE. While LLMs can support IE in various ways, their direct...

..., the trade-off between the robustness and training efficiency of PINNs is also discussed. The research findings show the potential of PINN models (particularly the PINN-Series) in advancing battery management systems, but the required computational resources should be considered. Artificial Intelligence...

...: [email protected] Email: [email protected] Artificial Intelligence Computer Aided Design Journal of Computing and Information Science in Engineering. Received November 22, 2024; Accepted manuscript posted May 13, 2025. doi:10.1115/1.4068664 Copyright © 2025 by ASME Accepted Manuscript...

.... 16 11 2024 04 05 2025 04 05 2025 12 05 2025 Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Artificial Intelligence Engineering Informatics Knowledge Engineering Accepted Manuscript...

.../1.4048204 [20] Kurrek , P. , Zoghlami , F. , Jocas , M. , Stoelen , M. , and Salehi , V. , 2020 , “ Q-Model: An Artificial Intelligence Based Methodology for the Development of Autonomous Robots ,” ASME J. Comput. Inf. Sci. Eng. , 20 ( 6 ), p. 061006 . 10.1115/1.4046992...

... 2025 network intervention multiagent system graph neural networks deep reinforcement learning hierarchical structure artificial intelligence engineering informatics multiscale modeling and simulation Many contemporary engineering systems—from smart grids and adaptive supply chains...

... 1 Corresponding author. Email: [email protected] artificial intelligence data-driven engineering machine learning for engineering applications References [1] Singh , D. D. , Mahender , T. , and Reddy , A. R. , 2021 , “ Powder Bed Fusion Process...

... and to predict system behavior accurately. Modern DTs may use a combination of 3D models, physics simulation, logic simulations, and artificial intelligence (AI) to accurately twin a real-world system. The uses of DTs can be broadly split into two categories: predictive and interrogative. Predictive uses...

...: [email protected] Email: [email protected] 1 Corresponding author. Email: [email protected] advanced computing infrastructure transformer based forecasting artificial intelligence big data and analytics data-driven engineering engineering informatics machine...

... artificial intelligence human–computer interfaces/interactions Physical human–robot interaction (pHRI) aims to integrate robotic systems with human cognitive abilities to perform tasks that exceed the capabilities of either entity alone [ 1 ]. Two critical aspects must be considered: (1) ensuring...

... Corresponding author. Email: [email protected] 19 04 2024 27 01 2025 07 02 2025 12 03 2025 human motion prediction gated recurrent unit Bayesian neural network long short-term memory human–robot collaboration disassembly remanufacturing artificial intelligence...

... action goals can significantly improve the HRC safety. National Science Foundation 10.13039/100000001 2026276 multi-task learning (MTL) human intent prediction human trajectory prediction human–robot collaboration (HRC) artificial intelligence machine learning for engineering...

[email protected] Email: [email protected] Email: [email protected] 13 12 2023 09 02 2025 12 02 2025 07 03 2025 artificial intelligence machine learning for engineering applications physics-based simulations process modeling for engineering applications In the heart...

... and Artificial Intelligence (ECAI) , Pitesti, Romania , June 27–29 , IEEE , pp. 1 – 6 . [15] Hu , Y. , Wu , X. , Zheng , G. , and Liu , X. , 2019 , “ Object Detection of UAV for Anti-UAV Based on Improved Yolo V3 ,” 2019 Chinese Control Conference (CCC), IEEE , pp. 8386 – 8390...

... network sparse voxel representation manufacturing feature recognition computer-aided process planning (CAPP) artificial intelligence cybermanufacturing manufacturing automation National Science Foundation 10.13039/100000001 2229260 Automatic manufacturing feature recognition...

... , A. , and Li , A. , 2020 , “ Orthogonal Gradient Descent for Continual Learning ,” International Conference on Artificial Intelligence and Statistics (AISTATS) , Palermo, Sicily, Italy , Aug. 26–28 , PMLR, pp. 3762 – 3773 . [31] Yoon , J. , Kim , S. , Yang , E. , and Hwang , S. J...

... for simulating, analyzing, and controlling their operation in real-time [ 12 , 13 ]. There has been a rapid increase in the usage of digital twins in industries, concurrent with advancements in machine learning, artificial intelligence, cloud computing, and data analytics [ 14 ]. Different industries...

.... In the final stage, latent needs are identified using an LLM on a provided criteria, and finally a report is generated from the identified latent needs. requirements elicitation large Language models artificial intelligence LLM agents computer-aided design Requirement elicitation (RE) sits...

[email protected] Email: [email protected] 23 04 2024 03 12 2024 08 12 2024 10 01 2025 artificial intelligence big data and analytics cybermanufacturing data-driven engineering knowledge engineering machine learning for engineering applications manufacturing automation...

... manufacturing. In recent years, with the rise of smart manufacturing and the Industry 4.0 paradigm, LFAM has been increasingly incorporating intelligent and automated technologies. Machine learning and artificial intelligence are now being utilized to refine the printing process and enhance the efficiency...