Abstract
Simulating and optimizing complex physical systems is known to be a task of considerable time and computational complexity. As a result, metamodeling techniques for the efficient exploration of the design space have become standard practice since they reduce the number of simulations needed. However, conventionally such metamodels are constructed sequentially in a one-shot manner, without exploiting inherent parallelism. To tackle this inefficient use of resources we present an adaptive framework where modeler and simulator interact through a distributed environment, thus decreasing model generation and simulation turnaround time. This paper provides evidence that such a distributed approach for adaptive sampling and modeling is worthwhile investigating. Research in this new field can lead to even more innovative automated modeling tools for complex simulation systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, G.G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. ASME Transactions. Journal of Mechanical Design, in press (2006)
Gu, L.: A comparison of polynomial based regression models in vehicle safety analysis. In: Diaz, A. (ed.) 2001 ASME Design Engineering Technical Conferences - Design Automation Conference, ASME, Pittsburgh (2001)
Marsden, A.L., et al.: Optimal aeroacoustic shape design using the surrogate management framework: Surrogate optimization. Optimization and Engineering 5(28), 235–262 (2004)
Simpson, T.W., et al.: Approximation methods in multidisciplinary analysis and optimization: A panel discussion. Structural and Multidisciplinary Optimization 27, 302–313 (2004)
Martin, J.D., Simpson, T.W.: Use of adaptive metamodeling for design optimization. In: Proc. of 9th AIAA/ISSMO symposium on multidisciplinary analysis and optimization, Atlanta, Georgia, 4-6 September (2002)
Hendrickx, W., Dhaene, T.: Multivariate modelling of complex simulation-based systems. In: Proceedings of the IEEE NDS 2005 conference, pp. 212–216. IEEE Computer Society Press, Los Alamitos (2005)
Hendrickx, W., Dhaene, T.: Sequential design and rational metamodelling. In: Kuhl, M.E., et al. (eds.) Proceedings of the 2005 Winter Simulation Conference, pp. 290–298 (2005)
Hendrickx, W., Dhaene, T.: M 3-toolbox (2005), Available on www.coms.ua.ac.be in the Software section
Gorissen, D., et al.: H2O Metacomputing - Jini Lookup and Discovery. In: Sunderam, V.S., et al. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 1072–1079. Springer, Heidelberg (2005)
Hellinckx, P., et al.: User experiences with nuclear physics calculations on H2O and on the BEgrid. In: Sunderam, V.S., et al. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 1081–1088. Springer, Heidelberg (2005)
modeFRONTIER, http://www.esteco.it/products/
FlightLab, http://www.flightlab.com/
Abramson, D., et al.: An automatic design optimization tool and its application to computational fluid dynamics. In: Proceedings of the 2001 ACM/IEEE conference on Supercomputing (CDROM), pp. 25–25. IEEE Computer Society Press, Los Alamitos (2001)
Abramson, D., Giddy, J., Kotler, L.: High performance parametric modeling with Nimrod/G: Killer application for the global grid? In: Rolim, J.D.P. (ed.) IPDPS-WS 2000. LNCS, vol. 1800, pp. 520–528. Springer, Heidelberg (2000)
Giunta, A., Eldred, M.: Implementation of a trust region model management strategy in the DAKOTA optimization toolkit. In: Proceedings of the 8th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Long Beach, CA (2000)
Eres, M.H., et al.: Implementation and utilisation of a grid-enabled problem solving environment in matlab. Future Generation Comp. Syst. 21, 920–929 (2005)
Ng, H.K., et al.: A multi-cluster grid enabled evolution framework for aerodynamic airfoil design optimization. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 1112–1121. Springer, Heidelberg (2005)
Ng, H.K., et al.: Grid enabled optimization. In: Sloot, P.M.A., et al. (eds.) EGC 2005. LNCS, vol. 3470, pp. 296–304. Springer, Heidelberg (2005)
Lehmensiek, R., Meyer, P.: Creating accurate multivariate rational interpolation models for microwave circuits by using efficient adaptive sampling to minimize the number of computational electromagnetic analyses. IEEE Trans. Microwave Theory Tech. 49, 1419 (2001)
De Geest, J., et al.: Adaptive CAD-model building algorithm for general planar microwave structures. IEEE Transactions on Microwave Theory and Techniques 47, 1801–1809 (1999)
Weiss, L., et al.: Bayesian computer-aided experimental design of heterogeneous scaffolds for tissue engineering. Computer Aided Design 37, 1127–1139 (2005)
Parmee, I., et al.: Towards autonomous evolutionary design systems via grid-based technologies. In: Proceedings of ASCE Computing in Civil Engineering, Cancun, Mexico (2005)
Casanova, H., et al.: The AppLeS parameter sweep template: User-level middleware for the grid. In: Proceedings of Supercomputing (SC 2000) (2000)
Casanova, H., et al.: Heuristics for scheduling parameter sweep applications in grid environments. In: Proc. 9th Heterogeneous Computing Workshop (HCW), Cancun, Mexico, pp. 349–363 (2000)
Berman, F., et al.: Adaptive computing on the grid using AppLeS. IEEE Transactions on Parallel and Distributed Systems (TPDS) 14, 369–382 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Gorissen, D., Crombecq, K., Hendrickx, W., Dhaene, T. (2007). Adaptive Distributed Metamodeling. In: Daydé, M., Palma, J.M.L.M., Coutinho, Á.L.G.A., Pacitti, E., Lopes, J.C. (eds) High Performance Computing for Computational Science - VECPAR 2006. VECPAR 2006. Lecture Notes in Computer Science, vol 4395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71351-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-71351-7_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71350-0
Online ISBN: 978-3-540-71351-7
eBook Packages: Computer ScienceComputer Science (R0)