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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures
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100%; open Pasquero C and Poletto M (2020) Find in CUMINCAD Deep Green , Proceedings of the 40th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA): Distributed Proximities, Virtual Conference. October 24-30, Delaware: ACADIA, 2020. pp. 668–677. http://papers.cumincad.org/cgi-bin/works/Show?acadia20_668
67%; open Arora, H, Langenhan, C, Petzold, F, Eisenstadt, V and Althoff, K-D (2020) Find in CUMINCAD Autocomplete designs by suggesting best practice design solutions in digital semantic building models (BIM) by combining AI approaches of case-based design and deep learning , Proceedings of ECPPM 2020, Moscow (accepted)
67%; open Arora, H, Langenhan, C, Petzold, F, Eisenstadt, V and Althoff, KD (2020) Find in CUMINCAD METIS-GAN: An approach to generate spatial configurations using deep learning and semantic building models , ECPPM 2020-2021
67%; open Arora, H, Langenhan, C, Petzold, F, Eisenstadt, V and Althoff, KD (2020) Find in CUMINCAD METIS-GAN: An approach to generate spatial configurations using deep learning and semantic building models , ECPPM-2020/21
67%; open Arora, H, Langenhan, C, Petzold,, F, Eisenstadt,, V and Althoff, K-D (2020) Find in CUMINCAD METIS-GAN: An approach to generate spatial configurations using deep learning and semantic building models , ECPPM-European Conference on Product and Process Modeling
67%; open Arshad, R., Zahoor, S., Shah, M. A., Wahid, A., & Yu, H. (2017) Find in CUMINCAD Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond , IEEE Access, 5, 15667–15681. https://doi.org/10.1109/ACCESS.2017.2686092
67%; open Ascione, F., Francesca De Masi R., Mastellone M., Ruggiero S., And Vanoli, G.P., (2020) Find in CUMINCAD Green Walls, a Critical Review: Knowledge Gaps, Design Parameters, Thermal Performances and Multi-criteria Design Approaches , Energies 13, (no. 9: 2296). Available at: https://doi.org/1.339/en1392296
67%; open Ascione, F.; De Masi, R, F; Mastellone, M; Ruggiero, S & Vanoli, G, P. (2020) Find in CUMINCAD Green walls, a critical review: Knowledge gaps, design parameters, thermal performances and multi-criteria design approaches , Energies, v. 13, n. 9
67%; open Bolojan, D. & Vermisso, E. (2020) Find in CUMINCAD Deep Learning as heuristic approach for architectural concept generation , Paper presented at the ICCC
67%; open Bolojan, D., & Vermisso, E. (2020) Find in CUMINCAD Deep Learning as heuristic approach for architectural concept generation Creativity and Artificial Intelligence , International Conference on Innovative Computing and Cloud Computing
67%; open Cai, C., and Li, B., (2021) Find in CUMINCAD Training deep convolution network with synthetic data for architectural morphological prototype classification , Frontiers of Architectural Research, 10(2020), 304-316
67%; open Cheong SY (2020) Find in CUMINCAD Getting started with image generation using tensorflow , Image Generation with Tensorflow: a practical guide to generating images and videos using deep learning. Birmingham, UK: Packt Publishing Ltd, 2020, pp. 4–24.
67%; open Dahngyu Cho, Jinsung Kim, Eunseo Shin, Jungsik Choi, and Jin-Kook Lee (2020) Find in CUMINCAD Recognizing Architectural Objects in Floor-Plan Drawings Using Deep-Learning Style-Transfer Algorithms , Proceedings of the 25th CAADRIA Conference. 717–725
67%; open David Newton, Dan Piatkowski, Wesley Marshall, and Atharva Tendle (2020) Find in CUMINCAD Deep Learning Methods for Urban Analysis and Health; Estimation of Obesity , ECAADe 2020: Health and Materials in Architecture and Cities. 297–304ol. 1. 297–304
67%; open De Miguel Rodríguez, J. et al. (2020) Find in CUMINCAD Generation of geometric interpolations of building types with deep variational autoencoders , Design Science, 6, p. e34
67%; open Erdemir, G., Zengin, A. T. & Akinci, T. C. (2020) Find in CUMINCAD Short-term wind speed forecasting system using deep learning for wind turbine applications , International Journal of Electrical and Computer Engineering, 10(6). https://doi.org/10.11591/ijece.v10i6.pp5779-5784
67%; open Fang, Zhihao (2020) Find in CUMINCAD Towards Multi-Drone Autonomous Construction via Deep Reinforcement Learning , Master’s thesis. Carnegie Mellon University
67%; open Fukuda, T, Novak, M, Fujii, H and Pencreach, Y (2020) Find in CUMINCAD Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness , International Journal of Architectural Computing (IJAC), 18(4), p. pgs 18
67%; open Fukuda, T, Novak, M, Fujii, H and Pencreach, Y (2020) Find in CUMINCAD Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness , International Journal of Architectural Computing, 16th September 2020, p. https://doi.org/10.1177/1478077120957544
67%; open Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., & Bennamoun, M. (2021) Find in CUMINCAD Deep Learning for 3D Point Clouds: A Survey , IEEE Trans Pattern Anal Mach Intell, 43(12), 4338-4364. https://doi.org/10.1109/TPAMI.2020.3005434

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