Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles

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  author =       "Changtong Luo and Zongmin Hu and Shao-Liang Zhang and 
                 Zonglin Jiang",
  title =        "Adaptive space transformation: An invariant based
                 method for predicting aerodynamic coefficients of
                 hypersonic vehicles",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "46, Part A",
  pages =        "93--103",
  year =         "2015",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2015.09.001",
  URL =          "",
  abstract =     "When developing a new hypersonic vehicle, thousands of
                 wind tunnel tests to study its aerodynamic performance
                 are needed. Due to limitations of experimental
                 facilities and/or cost budget, only a part of flight
                 parameters could be replicated. The point to predict
                 might locate outside the convex hull of sample points.
                 This makes it necessary but difficult to predict its
                 aerodynamic coefficients under flight conditions so as
                 to make the vehicle under control and be optimized.
                 Approximation based methods including regression,
                 nonlinear fit, artificial neural network, and support
                 vector machine could predict well within the convex
                 hull (interpolation). But the prediction performance
                 will degenerate very fast as the new point gets away
                 from the convex hull (extrapolation). In this paper, we
                 suggest regarding the prediction not just a
                 mathematical extrapolation, but a mathematics-assisted
                 physical problem, and propose a supervised
                 self-learning scheme, adaptive space transformation
                 (AST), for the prediction. AST tries to automatically
                 detect an underlying invariant relation with the known
                 data under the supervision of physicists. Once the
                 invariant is detected, it will be used for prediction.
                 The result should be valid provided that the physical
                 condition has not essentially changed. The study
                 indicates that AST can predict the aerodynamic
                 coefficient reliably, and is also a promising method
                 for other extrapolation related predictions.",
  keywords =     "genetic algorithms, genetic programming, Aerodynamic
                 coefficient, Data correlation, Scaling parameter,

Genetic Programming entries for Changtong Luo Zongmin Hu Shao-Liang Zhang Zonglin Jiang