| Deep learning for molecular design—a review of the state of the art DC Elton, Z Boukouvalas, MD Fuge, PW Chung Molecular Systems Design & Engineering 4 (4), 828-849, 2019 | 665 | 2019 |
| Applying machine learning techniques to predict the properties of energetic materials DC Elton, Z Boukouvalas, MS Butrico, MD Fuge, PW Chung Scientific Reports 8 (1), 9059-9070, 2018 | 249 | 2018 |
| Airfoil design parameterization and optimization using bézier generative adversarial networks W Chen, K Chiu, MD Fuge AIAA journal 58 (11), 4723-4735, 2020 | 146 | 2020 |
| Aerodynamic design optimization and shape exploration using generative adversarial networks W Chen, K Chiu, M Fuge AIAA Scitech 2019 forum, 2351, 2019 | 135 | 2019 |
| Conceptual design and modification of freeform surfaces using dual shape representations in augmented reality environments M Fuge, ME Yumer, G Orbay, LB Kara Computer-Aided Design 44 (10), 1020-1032, 2012 | 97 | 2012 |
| Diverse weighted bipartite b-matching F Ahmed, JP Dickerson, M Fuge Proceedings of the 26th International Joint Conference on Artificial …, 2017 | 84 | 2017 |
| Analysis of collaborative design networks: A case study of openideo M Fuge, K Tee, A Agogino, N Maton Journal of Computing and Information Science in Engineering 14 (2), 021009, 2014 | 83* | 2014 |
| Synthesizing designs with interpart dependencies using hierarchical generative adversarial networks W Chen, M Fuge Journal of Mechanical Design 141 (11), 111403, 2019 | 71 | 2019 |
| IH-GAN: A conditional generative model for implicit surface-based inverse design of cellular structures J Wang, WW Chen, D Da, M Fuge, R Rai Computer Methods in Applied Mechanics and Engineering 396, 115060, 2022 | 69 | 2022 |
| Design manifolds capture the intrinsic complexity and dimension of design spaces W Chen, M Fuge, J Chazan Journal of Mechanical Design 139 (5), 051102, 2017 | 61 | 2017 |
| B\'ezierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters W Chen, M Fuge arXiv preprint arXiv:1808.08871, 2018 | 60 | 2018 |
| Machine learning algorithms for recommending design methods M Fuge, B Peters, A Agogino Journal of Mechanical Design 136 (10), 101103, 2014 | 57 | 2014 |
| Machine learning for engineering design JH Panchal, M Fuge, Y Liu, S Missoum, C Tucker Journal of Mechanical Design 141 (11), 110301, 2019 | 56 | 2019 |
| Pattern analysis of IDEO's human-centered design methods in developing regions M Fuge, A Agogino Journal of mechanical Design 137 (7), 071405, 2015 | 45 | 2015 |
| Inverse design of two-dimensional airfoils using conditional generative models and surrogate log-likelihoods Q Chen, J Wang, P Pope, W Chen, M Fuge Journal of Mechanical Design 144 (2), 021712, 2022 | 37 | 2022 |
| How should we measure creativity in engineering design? A comparison between social science and engineering approaches SR Miller, ST Hunter, E Starkey, S Ramachandran, F Ahmed, M Fuge Journal of Mechanical Design 143 (3), 031404, 2021 | 37 | 2021 |
| Automatically Inferring Metrics for Design Creativity M Fuge, J Stroud, A Agogino ASME 2013 International Design Engineering Technical Conferences and …, 2013 | 37 | 2013 |
| Beyond the known: Detecting novel feasible domains over an unbounded design space W Chen, M Fuge Journal of Mechanical Design 139 (11), 111405, 2017 | 35 | 2017 |
| Discovering diverse, high quality design ideas from a large corpus F Ahmed, M Fuge, LD Gorbunov International Design Engineering Technical Conferences and Computers and …, 2016 | 35 | 2016 |
| Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora DC Elton, D Turakhia, N Reddy, Z Boukouvalas, MD Fuge, RM Doherty, ... arXiv preprint arXiv:1903.00415, 2019 | 31 | 2019 |
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