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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	Zhang, M., Li, P., Xia, Y., Wang, K., and Jin, L. (2020) Revisiting graph neural networks for link prediction , arXiv preprint arXiv:2010.16103

	Zhao H, Gu F, Huang QX, et al (2016) Connected fermat spirals for layered fabrication , ACM Trans Graph 2016; 35(4): 1–10

	Zhao H, Lu L, Wei Y, et al. (2016) Printed perforated lampshades for continuous projective images , ACM Trans Graph; 35(5): 154

	Zhao R, Ai T, Yu W, et al (2016) Recognition of building group patterns using graph convolutional network , Cartogr Geogr Inf Sci 2020; 47: 400–417.

	Zhao, J., M. Glueck, S. Breslav, F. Chevalier, And A. Khan (2017) Annotation Graphs: a Graph-based Visualization for Meta-analysis of Data Based on User-authored Annotations , IEEE Transactions on Visualization and Computer Graphics. Available at: https://doi.org/1.119/TVCG.216.2598543

	Zhao, P., Fei, Y., Huang, Y., Feng, Y., Liao, W., & Lu, X. (2023) Design-condition-informed shear wall layout design based on graph neural networks , Advanced Engineering Informatics, 58, 102190. https://doi.org/10.1016/j.aei.2023.102190

	Zhao, P., Fei, Y., Huang, Y., Feng, Y., Liao, W., & Lu, X. (2023) Design-condition-informed shear wall layout design based on graph neural networks , Advanced Engineering Informatics, 58, 102190, https://doi.org/10.1016/j.aei.2023.102190

	Zhao, P., Liao, W., Huang, Y., & Lu, X. (2023) Intelligent Beam Layout Design for Frame Structure Based on Graph Neural Networks , Journal of Building Engineering, 63, 15499. Available at: https://doi.org/1.116/j.jobe.222.15499

	Zhao, R., Ai, T., Yu, W., He, Y., & Shen, Y. (2020) Recognition of building group patterns using graph convolutional network , Cartography and Geographic Information Science, 47(5), 400-417

	Zhong, X., Koh, I. and Fricker, P. (2023) Building-GNN: Exploring a co-design framework for generating controllable 3D building prototypes by graph and recurrent neural networks , eCAADe 2023: Digital Design Reconsidered, Graz, Austria, pp. 431-440. Available at: https://doi.org/10.52842/conf.ecaade.2023.2.431

	Zhong, X., Koh, I., & Fricker, P. D. P. (2023) Building-GNN: Exploring a co-design framework for generating controllable 3D building prototypes by graph and recurrent neural networks , Digital Design Reconsidered: Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023), 431-440. https://doi.org/10.52842/conf.ecaade.2023.2.431

	Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M., (2020) Graph neural networks: A review of methods and applications , AI Open 1, 57-81. https://doi.org/https://doi.org/10.1016/j.aiopen.2021.01.001

	Zhou, X., Eibeck, A., Lim, M. Q., Krdzavac, N. B. & Kraft, M. (2019) An agent composition framework for the J-Park Simulator—A knowledge graph for the process industry , Computers & Chemical Engineering, 130, 106577

	Coderre, J. (2000) Machine Vision Illuminates the Future of Electronics , Photonics Spectra, December 2000, pp. 90-92

	Desroches, R., Comerio, M., Eberhard, M., Mooney, W., & Rix, G. (2011) Overview of the 2010 Haiti Earthquake , Earthquake Spectra, 27, S1-S21. doi:10.1193/1.3630129

	Stephenson, N. (1992) Snow Crash , Spectra Books

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