... 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...

... contribution of this work is the generic deep learning-based model that can effectively capture relationships between state and design decisions for an arbitrary action-space sequentially generative design problem. The framework is potentially adaptable for problems with a state representation of the form...

... 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...

... 2020 28 08 2020 18 09 2020 10 11 2020 LSTM time-variant reliability deep learning Gaussian process ensemble learning Reliability is the probability that an engineered system successfully performs its intended functionality with the consideration of various...

.... , 2012 , “ Determinantal Point Processes for Machine Learning ,” Found. Trends Mach. Learn. , 5 ( 2–3 ), pp. 123 – 286 . 10.1561/2200000044 [12] Goodfellow , I. , Bengio , Y. , and Courville , A. , 2016 , Deep Learning , MIT Press , Cambridge, MA . [13] Gmeiner , T...

... 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...

...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...

... 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...

... . Manuscript received March 24, 2018; final manuscript received July 25, 2018; published online October 1, 2018. Special Editor: Carolyn Seepersad. 24 03 2018 25 07 2018 microstructural materials design microstructural analysis deep learning generative adversarial network Bayesian...