Surrogate modelling for sustainable building design – A review
作者： Paul Westermann , Ralph Evins
DOI: 10.1016/J.ENBUILD.2019.05.057
关键词:
摘要: … the conceptual design stage, for sensitivity and uncertainty analysis, and for building design … As surrogate modelling may contribute to many sustainable building design problems, this …
sci-hub.se 参考文章(124)
Carl Edward Rasmussen, Gaussian processes in machine learning Lecture Notes in Computer Science. pp. 63- 71 ,(2003) , 10.1007/978-3-540-28650-9_4
Donald R. Jones, Matthias Schonlau, William J. Welch, Efficient Global Optimization of Expensive Black-Box Functions Journal of Global Optimization. ,vol. 13, pp. 455- 492 ,(1998) , 10.1023/A:1008306431147
J. Močkus, On bayesian methods for seeking the extremum Optimization Techniques IFIP Technical Conference Novosibirsk, July 1–7, 1974. pp. 400- 404 ,(1975) , 10.1007/3-540-07165-2_55
James Franklin, The elements of statistical learning : data mining, inference,and prediction The Mathematical Intelligencer. ,vol. 27, pp. 83- 85 ,(2005) , 10.1007/BF02985802
Alexander I. J Forrester, András Sóbester, A. J. Keane, Engineering Design via Surrogate Modelling: A Practical Guide ,(2008)
J. G. Carbonell, T. M. Mitchell, R. S. Michalski, Machine Learning: An Artificial Intelligence Approach Springer Publishing Company, Incorporated. ,(2013)
Yew Soon Ong, P. B. Nair, A. J. Keane, K. W. Wong, Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems Knowledge Incorporation in Evolutionary Computation. pp. 307- 331 ,(2005) , 10.1007/978-3-540-44511-1_15
Alex J. Smola, Bernhard Schölkopf, A tutorial on support vector regression Statistics and Computing. ,vol. 14, pp. 199- 222 ,(2004) , 10.1023/B:STCO.0000035301.49549.88
Elisabeth Gratia, André De Herde, A simple design tool for the thermal study of an office building Energy and Buildings. ,vol. 34, pp. 279- 289 ,(2002) , 10.1016/S0378-7788(01)00096-2
Athanasios Tsanas, Angeliki Xifara, Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools Energy and Buildings. ,vol. 49, pp. 560- 567 ,(2012) , 10.1016/J.ENBUILD.2012.03.003