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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	Yousif, S and Bolojan, D (2021) Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , Proceedings of CAADRIA 2021, pp. 151-160

	Yousif, S and Yan, W (2018) Clustering Forms for Enhancing Architectural Design Optimization , 23rd CAADRIA Conference: Learning, Adapting and Prototyping, Beijing, pp. 431-440

	Yousif, S. & Bolojan, D. (2021) Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , Projections, the 26th Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong, China. http://papers.cumincad.org/cgi-bin/works/paper/caadria2021_052

	Yousif, S., & Bolojan, D. (2021) Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , Proc. Of 26th CAADRIA

	Yousif, S., & Bolojan, D. (2021) Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems , 26th International Conference on Computer-Aided Architectural Design ResearchAsia: Projections, CAADRIA 2020 (pp. 151-160). The Association for Computer-Aided Architectural Design ResearchAsia (CAADRIA)

	Yousif, S., & Bolojan, D. (2021) Deep-performance: Incorporating deep learning for automating building performance simulation in generative systems , Projections - Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021, 1

	YOUSIF, S., & BOLOJAN, D. (2022) Deep learning-based surrogate modeling for performance-driven generative design systems , Proc of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), CAADRIA (pp. 363-372)

	Yousif, S., & Bolojan, D. (2022) Deep Learning-Based Surrogate Modeling for Performance-Driven Generative Design Systems , Proc of the 27th CAADRIA (pp. 363-372)

	Yousif, Shermeen, and Wei Yan. (2018) Clustering Forms for Enhancing Architectural Design Optimization , Learning, Adapting and Prototyping; The 23rd Conference of the Association for Computer-Aided Architectural Design Research Asia (CAADRIA). Beijing, China

	Yousof, G. S. (2001) Research into Wayang Kulit and teaching of the genre in institutions of higher learning in Malaysia , A. Rahim and A. Ghazali (eds. ), Proceedings of the Festival Wayang Nusantara: Wayang Dalam Bayang, pp. 96–120

	Yu, C., J. Liu, and Shamim Nemati (2020) Reinforcement Learning in Healthcare: A Survey , arXiv preprint. arXiv:1908.08796

	Yu, DK, Haeusler, MH, Simon, K and Fabbri, A (2018) BiCycle Pathway Generation Through a Weighted Digital Slime Mold Algorithm via Topographical Analysis , Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, pp. 381-390

	Yu, P., Wang, S. (2024) An Examination and Analysis of the Integration of Artificial Intelligence and Gamification in the Pedagogy of Chinese Higher Education. In Engaged Learning and Innovative Teaching in Higher Education , Digital Technology, Professional Competence, and Teaching Pedagogies, pp. 29-46, Singapore: Springer Nature Singapore

	Yu, T, Quillen, D, He, Z, Julian, R, Hausman, K, Finn, C and Levine, S (2020) Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning , Conference on Robot Learning, pp. 1094-1100

	Yu, T. et al. (2021) Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning , arXiv:1910.10897 [cs, stat] [Preprint]. Available at: http://arxiv.org/abs/1910.10897 (Accessed: 31 January 2022)

	Yuan, P. F., Chai, H., & Jin, J. (2018) Digital form-finding and fabrication of strained gridshells with complex geometries , Weiguo Xu (Ed.),23rd International Conference Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting (Vol. 1, pp. 267–276). CAADRIA

	Yukhina, E. (2007) Cognitive Abilities & Learning Styles in Design Processes and Judgements of Architecture Students , Ph.D. Dissertation, Faculty of Architecture, The University of Sydney

	Yunqin Li, Nobuyoshi Yabuki, and Tomohiro Fukuda (2022) Exploring the Association between Street Built Environment and Street Vitality Using Deep Learning Methods , Sustainable Cities and Society 79(April): 103656

	Zavoleas, Y (2015) The Model and Its Operative Significance in Architecture. Objects Driving Evolution in Design Research , Living and Learning: Research for a Better Built Environment: 49th International Conference of the Architectural Science Association, Melbourne, p. 1065-1074

	Zeng, D. and Sycara, K. (1997) Benefits of Learning in Negotiation , Proceedings of the National Conference on Artificial Intelligence (AAAI-97), American Association for Artificial Intelligence, pp. 36–41

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