... 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...

... of M echanical D esign . 10 05 2024 03 09 2024 05 09 2024 13 09 2024 Data-driven design design automation generative design Machine learning Division of Civil, Mechanical and Manufacturing Innovation 10.13039/100000147 2245299 Journal of Mechanical...

... , 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...

... Email: wangli@ge.com Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 13 03 2024 27 07 2024 01 08 2024 16 08 2024 artificial intelligence Data-driven design Machine learning robust design...

... 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...

... and concept generation machine learning Understanding and analyzing design requirements is a common challenge in design. Designers are often required to explore a range of potential ideas before choosing the appropriate and innovative solution to implement. In this process, early conceptualization...

... in the J ournal of M echanical D esign . 04 01 2024 22 04 2024 06 05 2024 03 06 2024 Graphical Abstract Figure design process machine learning Division of Civil, Mechanical and Manufacturing Innovation 10.13039/100000147 2322853 Design can...

... 09 04 2024 Graphical Abstract Figure data-driven design design of experiments design optimization design process machine learning simulation-based design Advanced Research Projects Agency - Energy 10.13039/100006133 DE-AR0001427 National Science Foundation...

...). The concept of latent crossover enables the integration of evolutionary algorithms and machine learning methods. In our future work, we plan to incorporate various types of evolutionary algorithms other than RCGAs, as well as VAE-based advanced machine learning methods into the proposed framework. In addition...

... 18 03 2024 Graphical Abstract Figure Bayesian optimization optimization-under-uncertainty efficient robust global optimization hypervolume expected improvement crash constraints Bayesian classification design optimization machine learning metamodeling multi-objective...

... 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...

... of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly...

... 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...

... Networks (IJCNN) , Padua, Italy , July 18–23 , pp. 1 – 8 . [5] Pascanu , R. , Mikolov , T. , and Bengio , Y. , 2013 , “ On the Difficulty of Training Recurrent Neural Networks ,” Proceedings of the 30th International Conference on Machine Learning , Atlanta, GA , June 17–19 , S...

... data to delineate feasible domains, accelerate optimization, or evaluate designs. However, the implementation of these methods usually demands machine learning expertise and multiple trials to choose the right method and hyperparameters. This makes them less accessible for numerous engineering...

... artificial intelligence machine learning product development international development evidence gap map In 2011, the U.S. Agency for International Development (USAID) released Evaluation Policy , and in doing so made an ambitious commitment to rigorously evaluating evidence in order to make...

... 2023 29 01 2024 design automation design optimization machine learning sensitivity analysis for design topology optimization Division of Graduate Education 10.13039/100000082 1842164 Office of Naval Research 10.13039/100000006 NAVAIR - Naval Air Systems Command...