Prediction of Convergence Dynamics of Design Performance using Differential Recurrent Neural Networks

Show simple item record Cao, Yi - Jin, Yaochu - Kowalczykiewicz, Michal - Sendhoff, Bernhard - 2014-04-15T04:01:09Z 2014-04-15T04:01:09Z 2008-12-31T00:00:00Z -
dc.identifier.citation Yi Cao, Yaochu Jin, Michal Kowalczykiewicz and Bernhard Sendhoff. Proceedings of the International Joint Conference on Neural Networks, 1-8 June 2008, Hong Kong. pp528-533
dc.identifier.isbn 9781424418213 -
dc.identifier.uri -
dc.description.abstract Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization. However, it normally takes very long time to solve such optimization problems due to the heavy computation load involved in CFD simulations, where a number of differential equations are to be solved. Some efforts have been seen using feedforward neural networks to approximate CFD models. However, feedforward neural network models cannot capture well the dynamics of the differential equations. Thus, training data from a large number of different designs are needed to train feedforward neural network models to achieve reliable generalization. In this work, a technique using differential recurrent neural networks has been proposed to predict the performance of candidate designs before the CFD simulation is fully converged. Compared to existing methods based on feedforward neural networks, this approach does not need a large number of previous designs. Case studies show that the proposed method is very promising. en_UK
dc.rights 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.title Prediction of Convergence Dynamics of Design Performance using Differential Recurrent Neural Networks en_UK
dc.type Conference paper -

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