Cumincad : CUMINCAD References : Search Results

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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	Eisenstadt, V, Arora, H, Ziegler, C, Bielski, J, Langenhan, C, Althoff, KD and Dengel, A (2021) Exploring optimal ways to represent topological and spatial features of building designs in deep learning methods and applications for architecture , CAADRIA 2021

	Eisenstadt, V, Arora, H, Ziegler, C, Langenhan, C and Althoff, K-D (2021) Exploring Optimal Ways to Represent Topological and Spatial Features of Building Designs in Deep Learning Methods and Applications for Architecture , 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia Online and Global

	Eisenstadt, V., Arora, H., Ziegler, C., Bielski, J., Langenhan, C., Althoff, K. D. & Dengel, A. (2021) Exploring optimal ways to represent topological and spatial features of building designs in deep learning methods and applications for architecture , CAADRIA 2021

	Eisenstadt, V., Arora, H., Ziegler, C., Bielski, J., Langenhan, C., Althoff, K. D. and Dengel, A. (2021) Exploring optimal ways to represent topological and spatial features of building designs in deep learning methods and applications for architecture , A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 191-200

	Eisenstadt, V., Arora, H., Ziegler, C., Bielski, J., Langenhan, C., Althoff, K.-D., Dengel, A., & Petzold, F. (2021) Exploring Optimal Ways to Represent Topological and Spatial Features of Building Designs in Deep Learning Methods and Applications for Architecture , Proceedings of Conference on Computer Aided Architectural Design Research in Asia (CAADRIA), 1, 191-2

	Eisenstadt, V., Arora, Hardik, Ziegler, C., Bielski, J., Langenhan, C., Althoff, K.-D., & Dengel, A. (2021) Comparative Evaluation of Tensor-based Data Representations for Deep Learning Methods in Architecture , V. Stojakovic & B. Tepavcevic (Eds.), Education and research Computer Aided Architectural Design Europe Conference. University of Novi Sad, 1, 45-54. Available at: https://doi.org/1.52842/conf.ecaade.221.1.45.

	Ekici, B., Kazanasmaz, Z. T., Turrin, M., TaSgetiren, M. F. & Sariyildiz, I. S. (2021) Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises , Part 1: Background, methodology, setup, and machine learning results. Solar Energy, 224, 373–389. https://doi.org/10.1016/j.solener.2021.05.083

	Engzell P, Frey A and Verhagen MD (2021) Learning loss due to school closures during the COVID-19 pandemic , PNAS;

	Estrina, T., Hui, V. & Ma, L. (2021) The Digital Design Build-Modes of Experiential Learning in the Pandemic Era , 26th International Conference on Computer-Aided Architectural Design Research Asia: Projections, CAADRIA 2021 (pp. 41-50). The Association for Computer-Aided Architectural Design Research Asia (CAADRIA)

	Fedorova, S., Tono, A., Nigam, M. S., Zhang, J., Ahmadnia, A., Bolognesi, C. & Michels, D. L. (2021) Synthetic 3d data generation pipeline for geometric deep learning in architecture , The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2021, 337–344

	Fei, M. A., Fr, A., Kfy, B., Yg, A., Cz, A., Dan, G. A. (2019) The spatial coupling effect between urban public transport and commercial complexes: A network centrality perspective - ScienceDirect , Sustainable Cities and Society, 50, 101645-101645.Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.Harris, P., Fotheringham, A. S., Crespo, R., Charlton, M. (2018). Inference in multi-scale geographically weighted regression. WILEY, 43(3), 399.Jia, J., Zhang, X. (2021). A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan. Journal of Cleaner Production, 316, 128321.Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., Fotheringham, A. S. (2019). MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. International Journal of Geo-Information, 8(6), 269.Rifkin, J. (2013). The Third Industrial Revolution. International Study Reference, 6(1), 8-11.Yu H, Fotheringham A S, Li Z, et al. (2019). Inference in multi-scale geographically weighted regression. Geographical Analysis, 52(1), 87-106

	Ferrara M, Della Santa F, Bilardo M, et al (2021) Design optimization of renewable energy systems for NZEBs based on deep residual learning , Renew Energ 2021; 176: 590–605

	Fletcher RR, Nakeshimana A and Olubeko O (2021) Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health , Frontiers in Artificial Intelligence 2021; 3(Article 561802).

	Fragkia, V., Foged, I. W. & Pasold, A. (2021) Predictive Information Modeling: Machine Learning Strategies for Material Uncertainty , Technology\|Architecture + Design, 5(2), 163–176. https://doi.org/10.1080/24751448.2021.1967057Grönquist, P., Wittel, F. K., & Rüggeberg, M. (2018). Modeling and design of thin bending wooden bilayers. PLOS ONE, 13(10), e0205607. https://doi.org/10.1371/journal.pone.0205607Grönquist, P., Wood, D., Hassani, M. M., Wittel, F. K., Menges, A., & Rüggeberg, M. (2019). Analysis of hygroscopic self-shaping wood at large scale for curved mass timber structures. Science Advances, 5(9), eaax1311. https://doi.org/10.1126/sciadv.aax1311Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621. https://doi.org/10.1109/TSMC.1973.4309314

	Fukuda, T., Novak, M., Fujii, H. and Pencreach, Y. (2021) Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness , International Journal of Architectural Computing, 19(2), pp. 190-207

	Giuseppe, P, Zhe, W, Abhishek, R, Tianzhen, H & Alfonso, C. (2021) Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives , Advances in Applied Energy, 5, 100084. https://doi.org/10.1016/j.adapen.2022.100084

	Goh GD, Sing SL and Yeong WY (2021) A review on machine learning in 3D printing: applications, potential, and challenges , Artif Intell Rev 2021; 54(1): 63–94

	Groschel, M. I., Owens, M., Freschi, L., Vargas, R., Marin, M. G., Phelan, J., ... & Farhat, M. R. (2021) Gentb: a User-friendly Genome-based Predictor for Tuberculosis Resistance Powered By Machine Learning , Genome medicine,13(1), 1-14. Available at: https://doi.org/1.1186/s1373-21-953-4.

	Guo, K. et al. (2021) Artificial Intelligence and Machine Learning in Design of Mechanical Materials , Materials Horizons, 8(4), pp. 1153-1172. DOI: 10.1039/D0MH01451F

	Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., & Bennamoun, M. (2021) 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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