... clustering convolutional neural network deep learning image retrieval patent image vision transformer National Science Council 10.13039/501100001868 Grant NSTC 113-2634-F-006-002 113-2221-E-007-113 Visual search is a convenient way to find an unknown object when we cannot describe...

... The training phase and inference phase steps are bounded by green dash-dot lines and red dashed lines, respectively planar 4- 6- and 8-bar linkage path synthesis variational autoencoder neural networks machine learning deep learning dataset generation data-driven design deep generative models...

...: matthew.jones@ll.mit.edu Email: faez@mit.edu Contributed by Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 10 07 2023 22 01 2024 29 01 2024 18 03 2024 Graphical Abstract Figure mesh deep learning graph neural...

... remotely deployed and paired with deep learning can enable practitioners to collect long-term data that provide insights that can be as beneficial as data collected through manual observation but with the cost and continuity made possible by sensor devices. Postulates related to successfully developing...

... the conventional ETRA problem into the classification of three classes of joint focal elements (JFEs) and then solves the classification problem effectively through a deep learning approach. The core of solving an ETRA problem is to determine whether the joint focal element is located in the reliable region...

... paramount aspects. When applying deep learning-based methods to product shape design, two major challenges exist: (1) design data exhibit in multiple modalities and (2) an increasing demand for creativity. With recent advances in deep learning of cross-modal tasks (DLCMTs), which can transfer one design...

...Ayush Raina; Jonathan Cagan; Christopher McComb Building an Artificial Intelligence (AI) agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However...

...Yuyang Wang; Joe Joseph; T. P. Aniruddhan Unni; Soji Yamakawa; Amir Barati Farimani; Kenji Shimada Design and optimization of hull shapes for optimal hydrodynamic performance have been a major challenge for naval architectures. Deep learning bears the promise of comprehensive geometric...

... 2021 22 09 2021 11 10 2021 decision making complex action spaces imitation learning design automation deep learning action prediction artificial intelligence machine learning Defense Advanced Research Projects Agency 10.13039/100000185 N66001-17-1-4064...

... beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems...

... learning utilizes a layered structure of algorithms to make informed decisions based on the knowledge learned from data. In recent years, deep learning techniques have been successfully applied in a variety of applications such as image processing [ 26 , 27 ], natural language processing [ 28 ], medical...

... of M echanical D esign . 18 05 2020 08 08 2020 22 08 2020 10 11 2020 design automation design optimization design representation generative design deep learning simulation-based design A designer wants good design solutions, which are creative and meet...

... perturbation to even a single position of a given motion can change the topology and dimensions of the synthesized mechanisms drastically. Thus, the synthesis becomes a blind iterative process of maneuvering precision positions in the hope of finding good solutions. In this paper, we present a deep-learning...

... optimization generative design multidisciplinary design and optimization deep learning deep reinforcement learning Expensive design explorations often arise in several engineering problems. Solving the inverse problems for the design of micro-structures [ 1 ], manufacturing processes [ 2...

...Sangeun Oh; Yongsu Jung; Seongsin Kim; Ikjin Lee; Namwoo Kang Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work...

... arbitrary sized microstructures. While the deep learning network is trained by setting the dimension of z as bs . × 4 × 4 × 1, one may modify the dimensionality of latent variables z to control the size of the generated images without retraining the model. Figure 9 illustrates the generated images...