...Mohamed Elrefaie; Angela Dai; Faez Ahmed This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network model, both aimed at aerodynamic car design through machine learning. DrivAerNet...

...@dlut.edu.cn artificial intelligence computer-aided design Data-driven design generative design Machine learning multiobjective optimization Journal of Mechanical Design. Received October 29, 2024; Accepted manuscript posted March 5, 2025. doi:10.1115/1.4068106 Copyright © 2025 by ASME GraphDGM...

... datasets have been published as benchmarks; however, most existing datasets are unimodal, offering only image-based 3 [ 13 ] or parametric 4 representations of UAVs. These datasets support the development of machine learning algorithms...

... design design automation design for manufacturing design integration design optimization design validation design visualization interactive design Machine learning metamodeling multidisciplinary design and optimization reliability in design robust design sensitivity analysis for design...

... for implementing machine-learning models. The power system networks are represented as graphs with features holding information on the nodes and edges that relate to electrical components. Using the generated dataset, this research implements a generative approach based on Wasserstein generative adversarial...

... 12 12 2024 11 02 2025 Email: ssridhara@wisc.edu 1 Corresponding author. Email: ksuresh@wisc.edu data-driven design design representation machine learning Algorithm 1 Encode Materials  1: procedure M at E ncode ( ζ )   ⊳ Input: Training data  2:  epoch = 0...

... of M echanical D esign . 10 07 2024 13 12 2024 16 12 2024 30 01 2025 machine learning topology optimization lattice structure The pursuit of lightweight, high-performance parts justifies the use of additive manufacturing to fabricate complex geometries [ 1...

... a variety of design briefs and criteria. This dataset is designed to serve as a benchmark to facilitate the evaluation and modification of machine learning models in the context of material selection for conceptual design. By focusing on a diverse set of design tasks and criteria, MSEval enables a more...

... Data-driven design Machine learning National Science Foundation 10.13039/100000001 Engineering Design and Systems Engineering (EDSE) grant #2050052 Journal of Mechanical Design. Received June 03, 2024; Accepted manuscript posted January 10, 2025. doi:10.1115/1.4067619 Copyright © 2025...

...Kevin Ferguson; Yu-hsuan Chen; Yiming Chen; Andrew Gillman; James Hardin; Levent Burak Kara Machine-learned surrogate models to accelerate lengthy computer simulations are becoming increasingly important as engineers look to streamline the product design cycle. In many cases, these approaches offer...

...: ankush.kapoor@unsw.edu.au coronary stent design computer-aided design design optimization design representation machine learning product design Algorithm 1 Surrogate assisted optimization framework Require: Evaluated LHS design solutions X l , objective function value of evaluated...

...Anar Nurizada; Rohit Dhaipule; Zhijie Lyu; Anurag Purwar In recent years, there has been a strong interest in applying machine learning techniques to path synthesis of linkage mechanisms. However, progress has been stymied due to a scarcity of high-quality datasets. In this article, we present...

... 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...

... automation generative design machine learning Division of Civil, Mechanical and Manufacturing Innovation 10.13039/100000147 2245299 In computer graphics and the computer-aided design (CAD)/computer-aided manufacturing (CAM) industry, parametric smooth curves (e.g., Bézier, B-spline...

... Graphical Abstract Figure porous metamaterial graph neural network variational graph autoencoder design optimization generative design connectivity data-driven design design of engineered materials system machine learning Division of Civil, Mechanical and Manufacturing Innovation...

... , A. , and Tan , C. , 2020 , “ Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations ,” ACM Conference on Fairness, Accountability, and Transparancy , Barcelona, Spain , Jan. 27–30 , pp. 607 – 617 . [12] Poyiadzi , R. , Sokol , K. , Santos-Rodriguez , R...

...Milad Habibi; Shai Bernard; Jun Wang; Mark Fuge When performing time-intensive optimization tasks, such as those in topology or shape optimization, researchers have turned to machine-learned inverse design (ID) methods—i.e., predicting the optimized geometry from input conditions—to replace or warm...

... customizable sit-to-stand (STS) mechanical device by integrating rigid body kinematics with machine learning. Unlike traditional mechanism synthesis approaches that primarily focus on limited functional requirements, such as path or motion generation, our proposed design pipeline efficiently generates a large...

... human–computer interaction transparency robustness fairness experimental study artificial intelligence cognitive-based design design theory and methodology machine learning References [1] Asan , O. , Bayrak , A. E. , and Choudhury , A. , 2020 , “ Artificial...

... 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...