...@andrew.cmu.edu Email: conradt@andrew.cmu.edu Email: cagan@cmu.edu design automation Machine learning mechanism synthesis Journal of Mechanical Design. Received July 04, 2022; Accepted manuscript posted March 08, 2023. doi:10.1115/1.4062147 Copyright © 2023 bJyouArSnMal Eof Mechanical Design...

... Networks: Feed-Forward Synthesis of Textures and Stylized Images ,” Proceedings of the 33rd International Conference on International Conference on Machine Learning, Volume 48 , New York , June 9–14 , pp. 1349 – 1357 . [35] Jing , Y. , Yang , Y. , Feng , Z. , Ye , J. , Yu , Y...

... Email: gpw1@psu.edu Contributed by Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 23 08 2022 05 01 2023 09 01 2023 10 02 2023 design process machine learning Pennsylvania State University 10.13039/100008321...

...., texts or sketches). To overcome these limitations, we propose an attention-enhanced multimodal learning (AEMML)-based machine learning (ML) model to predict five design metrics: drawing quality, uniqueness, elegance, usefulness, and creativity. The proposed model utilizes knowledge from large external...

...] Kusner , M. , Sun , Y. , Kolkin , N. , and Weinberger , K. , 2015 , “ From Word Embeddings to Document Distances ,” Proceedings of the International Conference on Machine Learning , Lille, France , July 7–9 , PMLR, pp. 957 – 966 . [47] Kilickaya , M. , Erdem...

...@aero.bombardier.com Email: lan.lin@concordia.ca Email: nadia.bhuiyan@concordia.ca Email: cheligeer.cheligeer@mail.concordia.ca semantic network computer-aided design conceptual design data-driven design machine learning recursive object model References [1] Zeng , Y. , 2011...

... 2022 09 12 2022 design automation artificial intelligence deep learning generalizability agent-based design data-driven design design representation generative design machine learning Monte Carlo Tree Search Solving design problems is one of the most ubiquitous processes...

... processing multi-instance inference language model text-to-text transfer transformer (T5) artificial intelligence machine learning product development References [1] Schaffhausen , C. R. , and Kowalewski , T. M. , 2015 , “ Large-Scale Needfinding: Methods of Increasing...

... in the J ournal of M echanical D esign . 03 07 2022 14 10 2022 29 10 2022 24 11 2022 ensemble remaining useful life lithium-ion batteries rolling element bearings machine learning metamodeling Accurately predicting the remaining useful life (RUL) of equipment...

... systems yet. In the machine learning community, various algorithms, such as graph recurrent neural network [ 19 ], graph recurrent attention network [ 20 ], and graph variational autoencoder (GVAE) [ 21 ], have been proposed to generate synthetic graph/discrete structures. And with the help of the graph...

... are increasingly employed to accelerate nonlinear materials modeling by using a combination of methods from linear algebra and machine learning that result in reducing the number of unknown variables that characterize, e.g., microstructural strain and stress fields. Transformation field analysis (TFA) and its...

...Doksoo Lee; Yu-Chin Chan; Wei (Wayne) Chen; Liwei Wang; Anton van Beek; Wei Chen Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model...

.... , Chen , Y. , Belov , D. , and de Freitas , N. , 2017 , “ Parallel Multiscale Autoregressive Density Estimation ,” Proceedings of the 34th International Conference on Machine Learning , Sydney, Australia , July , Vol. 70, JMLR, pp. 2912 – 2921 . [62] Chen , Y. , Tu , S...

... of Near-Optimal Topology Through Theory-Driven Machine Learning ,” Comput. Aided Des. , 109 , pp. 12 – 21 . 10.1016/j.cad.2018.12.008 [18] Yu , Y. , Hur , T. , Jung , J. , and Jang , I. G. , 2019 , “ Deep Learning for Determining a Near-Optimal Topological Design Without Any...

... Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 08 02 2022 11 07 2022 15 07 2022 08 08 2022 artificial intelligence machine learning systems design systems engineering reinforcement learning interpretable AI...

... is investigated using multiple machine learning models including Gaussian process regression and neural networks. A case study demonstrates the advantages of the proposed surrogate modeling method using a dynamic ensemble of NARX models (DENA) over several classical surrogate modeling methods. Aiming...

...) features. PAS features may not contribute to engineered sustainability but are identified by customers as sustainable. In previous papers, we proposed methods for extracting PAS features from online reviews using machine learning techniques and validating them using collage placement techniques. We...

... 07 2022 machine learning metamodeling reliability in design uncertainty analysis National Natural Science Foundation of China 10.13039/501100001809 51875525 51905475 52111540267 Natural Science Foundation of Zhejiang Province 10.13039/501100004731 LY21E050008...

... of the total resistance of the ship hull structure while avoiding the inconsistent estimates from different types of design input parameters. It is demonstrated that the developed AI-based machine learning algorithm as a prediction tool can assist the ship hull design process by accurately providing the total...

... in the J ournal of M echanical D esign . 01 12 2021 28 04 2022 04 05 2022 13 06 2022 Gaussian processes data fusion calibration emulation manifold learning machine learning metamodeling uncertainty modeling National Aeronautics and Space Administration...