BibTeX
@INCOLLECTION{
         Padulo2008RAC,
       author = "Mattia Padulo and Shaun A. Forth and Marin D. Guenov",
       title = "Robust Aircraft Conceptual Design Using Automatic Differentiation in {M}atlab",
       doi = "10.1007/978-3-540-68942-3_24",
       pages = "271--280",
       abstract = "The need for robust optimisation in aircraft conceptual design, for which the
         design parameters are assumed stochastic, is introduced. We highlight two approaches, first-order
         method of moments and Sigma-Point reduced quadrature, to estimate the mean and variance of the
         design's outputs. The method of moments requires the design model's differentiation and
         here, since the model is implemented in Matlab, is performed using the automatic differentiation
         (AD) tool MAD. Gradient-based constrained optimisation of the stochastic model is shown to be more
         efficient using AD-obtained gradients than finite-differencing. A post-optimality analysis,
         performed using AD-enabled third-order method of moments and Monte-Carlo analysis, confirms the
         attractiveness of the Sigma-Point technique for uncertainty propagation.",
       crossref = "Bischof2008AiA",
       booktitle = "Advances in Automatic Differentiation",
       publisher = "Springer",
       editor = "Christian H. Bischof and H. Martin B{\"u}cker and Paul D. Hovland and Uwe
         Naumann and J. Utke",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2008",
       ad_area = "Computational Fluid Dynamics",
       ad_tools = "TOMLAB /MAD"
}