... networking, by connecting customers based on feature importance. We further validated the effectiveness of the framework using three real-world datasets and demonstrated the proposed method outperformed the previous approach. Contributed by the Design Automation Committee of ASME for publication...

...%, demonstrating the efficiency of this method for lightweight hydraulic equipment design. Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 05 06 2024 12 09 2024 18 09 2024 25 09 2024 Email: jyao@ysu.edu.cn...

... 2024 12 09 2024 25 09 2024 Email: seyoung7@illinois.edu Email: klin14@illinois.edu Email: june30@hanyang.ac.kr Email: hmkim@illinois.edu Data-driven design design automation Accepted Manuscript Not Copyedited Journal of Mechanical Design. Received April 02, 2024...

... of autocompleting a volumetric design sequence from a partial design sequence. Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 21 04 2024 02 09 2024 09 09 2024 25 09 2024 Email: alam.92@osu.edu Email: yi.wang...

... of the medium-size SUV model. Email: jicezeng@umich.edu Email: zgao@ford.com Email: yli197@ford.com Email: sbarbat@ford.com Email: jinluz@umich.edu 1 Corresponding author. Email: zhennhu@umich.edu Contributed by the Design Automation Committee of ASME for publication in the J...

..., and aesthetically coherent designs, marking a significant advancement by merging classical geometrical techniques with modern deep learning. Email: jwang22@scu.edu Email: syang11@scu.edu Email: kwang4@scu.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal...

...-objective counterfactuals (MOCs) [ 13 ]. Counterfactual explanations are a much-needed alternative to popular “feature importance” methods [ 14 , 15 ] in machine learning explainability. In the design automation community, these “feature importance” methods are often used to determine which design...

... in computational engineering design. 1 Corresponding author. Email: soheyl.massoudi@epfl.ch Email: cyrilp@mit.edu Email: jurg.schiffmann@epfl.ch Contributed by Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 22 03 2024 18 06 2024...

... characteristics often observed in industrial applications. Email: jeweisla@uci.edu Email: chuanniz@uci.edu 1 Corresponding author. Email: won@uci.edu 1 Corresponding author. Email: raminb@uci.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal...

...Amin Heyrani Nobari; Justin Rey; Suhas Kodali; Matthew Jones; Faez Ahmed In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine learning-based scheme...

...@illinois.edu Email: zhengl6@illinois.edu 1 Corresponding author. Email: pingfeng@illinois.edu Email: yumengl@illinois.edu Contributed by Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 03 09 2023 04 01 2024 09 01 2024 05...

... optimization sparse samples extreme scenario linear moment estimation design automation multidisciplinary design and optimization Graphical Abstract Figure 13 12 2023 25 01 2024 25 01 2024 05 03 2024 Contributed by the Design Automation Committee of ASME...

... in the future. The method presented here is expected to lead to faster development of holistic SPI2 design automation methods which are robust, efficient, and provide better design solutions than purely manual or heuristic design methods. Some major advantages of the proposed design optimization method...

...-source code is provided through a python repository. Email: jnajmon@purdue.edu 1 Corresponding author. Email: tovara@iupui.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 15 07 2023 27 11 2023 30 11...

... Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 15 07 2023 08 12 2023 11 12 2023 29 01 2024 machine learning design regression Gaussian process data-driven design design automation design optimization...

...: lalfred@student.ethz.ch Email: birnere@student.ethz.ch Email: tlumpe@ethz.ch Email: tinos@ethz.ch Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 07 08 2023 28 11 2023 30 11 2023 12 01 2024...

... Corresponding author. Email: bex@andrew.cmu.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 22 06 2023 29 11 2023 30 11 2023 12 01 2024 design optimization generative design micro and nanosystems design...

... data [ 5 – 7 ]. Recent advancements have also demonstrated the potential of advanced AI-like generative pretrained transformers (GPT) for automatic design concept generation [ 8 , 9 ]. artificial intelligence computer-aided design data-driven design design automation design for humans...

... the proposed approaches and to demonstrate their efficacy in improving the prediction accuracy of the CAE models. Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 09 08 2023 20 10 2023 26 10 2023 15 12 2023...

... and the approach in practice is also outlined. Email: glzhang@cmu.edu Email: cagan@cmu.edu 17 03 2023 31 07 2023 03 08 2023 06 12 2023 constraint satisfaction design optimization engineering design GPU programming interval arithmetic parallel computing design automation...