... of ASME for publication in the J ournal of M echanical D esign . 03 04 2024 18 11 2024 01 12 2024 06 12 2024 artificial intelligence collaborative design conceptual design Data-driven design design representation metamodeling National Natural Science...

...Seongjun Hong; Yongmin Kwon; Dongju Shin; Jangseop Park; Namwoo Kang Recent advances in artificial intelligence (AI) have impacted various fields, including mechanical engineering. However, the development of diverse, high-quality datasets for structural analysis remains a challenge. Traditional...

... Committee of ASME for publication in the J ournal of M echanical D esign . 21 04 2024 02 09 2024 09 09 2024 18 11 2024 Graphical Abstract Figure artificial intelligence computational geometry design automation design representation generative design...

... artificial intelligence computational kinematics computer-aided engineering data-driven design generative design linkages mechanism synthesis Path-generating linkage mechanisms are fundamental in mechanical design due to their ability to translate rotational or linear input into specific...

... artificial intelligence computer-aided engineering data-driven design design for manufacturing design process machine learning simulation-based design References [1] Rankin , J. J. , and Ott , D. A. , 1992 , “ The Open Approach to FEA Integration in the Design Process...

...@utdallas.edu alternative energy system design artificial intelligence Data-driven design Journal of Mechanical Design. Received June 25, 2024; Accepted manuscript posted October 07, 2024. doi:10.1115/1.4067056 Copyright © 2024 by ASME Accepted Manuscript Not Copyedited Bi-Level Inter-Turn Short...

... Abstract Figure surrogate modeling image-based surrogates deep neural networks dimensionality reduction artificial intelligence data-driven design machine learning robust design uncertainty modeling Office of Energy Efficiency and Renewable Energy 10.13039/100006134 DE...

... ournal of M echanical D esign . 20 05 2024 23 08 2024 26 08 2024 23 09 2024 Graphical Abstract Figure CAE model bias correction CycleGAN transfer learning data augmentation artificial intelligence computer-aided engineering design automation...

.... , De Bie , T. , and Flach , P. , 2020 , “ Face: Feasible and Actionable Counterfactual Explanations ,” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society , New York , Feb. 7–8 , pp. 344 – 350 . [13] Dandl , S. , Molnar , C. , Binder , M. , and Bischl...

...Mostaan Lotfalian Saremi; Isabella Ziv; Onur Asan; Alparslan Emrah Bayrak Intelligent systems have been rapidly evolving and play a pivotal role in assisting individuals across diverse domains, from healthcare to transportation. Understanding the dynamics of human–artificial intelligence (AI...

...Xiaoneng Jin; Hua Dong; Mark Evans; Anqi Yao Artificial intelligence (AI) has the potential to revolutionize product design, and designers need to know how to best leverage its capabilities. Based on the concept–knowledge (C-K) theory, a set of inspirational stimuli (IS) for the design of AI...

... constrained multi-objective optimization robustness metrics computational efficiency artificial intelligence data-driven design design automation design for manufacturing design integration design of multiscale systems design optimization machine learning metamodeling Designing...

...Binyang Song; Chenyang Yuan; Frank Permenter; Nikos Arechiga; Faez Ahmed Generative artificial intelligence (AI) models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating...

... problem. artificial intelligence design methodology design optimization machine learning metamodeling multi-objective optimization systems design Group behavior is widespread with phenomena like ant colonies, fish swarms, bird flights, and so on. The industry has been inspired...

...Leah Chong; Kenneth Kotovsky; Jonathan Cagan As artificial intelligence (AI) systems become increasingly capable of performing design tasks, they are expected to be deployed to assist human designers' decision-making in a greater variety of ways. For complex design problems such as those...

... 05 03 2024 Graphical Abstract Figure multi-fidelity surrogate neural network machine learning mapping model different input spaces artificial intelligence computer-aided engineering metamodeling Output data from engineering systems, whether observed or predicted...

... , A. , Chen , M. , Zhang , L. , and Xu , Q. , 2023 , “ Are Transformers Effective for Time Series Forecasting? ,” Proceedings of the AAAI Conference on Artificial Intelligence , Washington, DC , Feb. 7–14 , Vol. 37 , pp. 11121 – 11128 . [33] Hochreiter , S. , and Schmidhuber , J...

.../ Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 22 09 2023 12 02 2024 13 02 2024 05 03 2024 Graphical Abstract Figure artificial intelligence data-driven design design evaluation machine learning...

... is time and resource intensive, and is often done by manual screening. In this study, we develop an artificial intelligence (AI) agent based on a bidirectional encoder representations from transformers (BERT) model and incorporate it into a human team designing an evidence synthesis product for global...

...Zeda Xu; Chloe Soohwa Hong; Nicolás F. Soria Zurita; Joshua T. Gyory; Gary Stump; Hannah Nolte; Jonathan Cagan; Christopher McComb Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem-solving has been the focus of intensive ongoing research. However...