28,845 research outputs found
High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition
This paper presents a novel adaptive-sparse polynomial dimensional
decomposition (PDD) method for stochastic design optimization of complex
systems. The method entails an adaptive-sparse PDD approximation of a
high-dimensional stochastic response for statistical moment and reliability
analyses; a novel integration of the adaptive-sparse PDD approximation and
score functions for estimating the first-order design sensitivities of the
statistical moments and failure probability; and standard gradient-based
optimization algorithms. New analytical formulae are presented for the design
sensitivities that are simultaneously determined along with the moments or the
failure probability. Numerical results stemming from mathematical functions
indicate that the new method provides more computationally efficient design
solutions than the existing methods. Finally, stochastic shape optimization of
a jet engine bracket with 79 variables was performed, demonstrating the power
of the new method to tackle practical engineering problems.Comment: 18 pages, 2 figures, to appear in Sparse Grids and
Applications--Stuttgart 2014, Lecture Notes in Computational Science and
Engineering 109, edited by J. Garcke and D. Pfl\"{u}ger, Springer
International Publishing, 201
Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling
Gaussian process emulators of computationally expensive computer codes
provide fast statistical approximations to model physical processes. The
training of these surrogates depends on the set of design points chosen to run
the simulator. Due to computational cost, such training set is bound to be
limited and quantifying the resulting uncertainty in the hyper-parameters of
the emulator by uni-modal distributions is likely to induce bias. In order to
quantify this uncertainty, this paper proposes a computationally efficient
sampler based on an extension of Asymptotically Independent Markov Sampling, a
recently developed algorithm for Bayesian inference. Structural uncertainty of
the emulator is obtained as a by-product of the Bayesian treatment of the
hyper-parameters. Additionally, the user can choose to perform stochastic
optimisation to sample from a neighbourhood of the Maximum a Posteriori
estimate, even in the presence of multimodality. Model uncertainty is also
acknowledged through numerical stabilisation measures by including a nugget
term in the formulation of the probability model. The efficiency of the
proposed sampler is illustrated in examples where multi-modal distributions are
encountered. For the purpose of reproducibility, further development, and use
in other applications the code used to generate the examples is freely
available for download at https://github.com/agarbuno/paims_codesComment: Computational Statistics \& Data Analysis, Volume 103, November 201
Analytical reliability calculation of linear dynamical systems in higher dimensions
The recent application of reliability analysis to controller synthesis has created the need for a
computationally efficient method for the estimation of the first excursion probabilities for linear dynamical
systems in higher dimensions. Simulation methods cannot provide an adequate solution to this specific application,
which involves numerical optimization of the system reliability with respect to the controller parameters,
because the total computational time needed is still prohibitive. Instead, an analytical approach is presented
in this paper. The problem reduces to the calculation of the conditional upcrossing rate at each surface
of the failure boundary. The correlation between upcrossings of the failure surface for the different failure
events may be addressed by the introduction of a multi-dimensional integral. An efficient algorithm is
adopted for the numerical calculation of this integral. Also, the problem of approximation of the conditional
upcrossing rate is discussed. For the latter there is no known theoretical solution. Three of the semi-empirical
corrections that have been proposed previously for scalar processes are compared and it is shown that the correction
should be based on the bandwidth characteristics of the system. Finally, examples that verify the validity
of the analytical approximations for systems in higher dimensions are discussed
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