...: gabrielemilio.garayalde@polimi.it agent-based design computer-aided engineering design optimization design process Machine learning Journal of Mechanical Design. Received May 30, 2024; Accepted manuscript posted March 27, 2025. doi:10.1115/1.4068300 Copyright © 2025 by ASME; reuse license CC-BY 4.0 Gabriel...

...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 for regression, both aimed at aerodynamic car design through machine learning...

... for discontinuous and non-differentiable functions. Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 06 07 2024 08 03 2025 13 03 2025 21 03 2025 Email: anton.vanbeek@ucd.ie design optimization Machine learning...

... machine learning Defense Advanced Research Projects Agency 10.13039/100000185 N66001-17-1-4064 Data-driven design and, to an increasing degree, artificial intelligence (AI)-driven design, are important processes in the development and deployment of cyberphysical...

... 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...

... design design representation machine learning Office of Naval Research 10.13039/100000006 Panther Award N00014-21-1-2916 through Dr. Timothy Bentley Representations form the backbone of engineering advances. For example, geometric representations such as constructive solid...

... 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...

...-assisted optimization algorithms that can search across multiple stent design concepts in future works. The underlying surrogates in such models can be further improved using hyper-parameter tuning. Recent developments in machine learning can also eliminate the notion of design concept altogether, wherein...

...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...

... Optimization: Inspired by Nature and Guided by Machine Learning ,” Adv. Mater. , 34 ( 3 ), p. 2106248 . 10.1002/adma.202106248 [32] Ma , J. , Huang , Y. , Pu , M. , Xu , D. , Luo , J. , Guo , Y. , and Luo , X. , 2020 , “ Inverse Design of Broadband Metasurface Absorber...

... generative design machine learning multi-objective optimization Modifying existing designs to generate new ones is an essential aspect of various engineering sectors, such as aerospace, automotive, architecture, pharmaceuticals, consumer goods, and many others. Design modification significantly...

...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...

... Abstract Figure human–computer interaction transparency robustness fairness experimental study artificial intelligence cognitive-based design design theory and methodology machine learning The capabilities of artificial intelligence (AI) systems have rapidly enhanced in the last...