The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Ali Elham - One of the best experts on this subject based on the ideXlab platform.
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Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-Stationary Gaussian Process
Structural and Multidisciplinary Optimization, 2015Co-Authors: Robin Dufour, Julien Muelenaere, Ali ElhamAbstract:This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization Process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using empirical formulas. The trajectory was optimized using a pseudo-spectral approach with an automated mesh refinement that allowed for increasing the sparsity of the Jacobian of the constraints. The aerodynamics of the spaceplane was computed using an Euler code and the results were used to create a surrogate model based on a non-Stationary Gaussian Process procedure that was specially developed for this study.
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Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-Stationary Gaussian Process
Structural and Multidisciplinary Optimization, 2015Co-Authors: Robin Dufour, Julien Muelenaere, Ali ElhamAbstract:This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization Process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using empirical formulas. The trajectory was optimized using a pseudo-spectral approach with an automated mesh refinement that allowed for increasing the sparsity of the Jacobian of the constraints. The aerodynamics of the spaceplane was computed using an Euler code and the results were used to create a surrogate model based on a non-Stationary Gaussian Process procedure that was specially developed for this study.
Robin Dufour - One of the best experts on this subject based on the ideXlab platform.
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Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-Stationary Gaussian Process
Structural and Multidisciplinary Optimization, 2015Co-Authors: Robin Dufour, Julien Muelenaere, Ali ElhamAbstract:This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization Process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using empirical formulas. The trajectory was optimized using a pseudo-spectral approach with an automated mesh refinement that allowed for increasing the sparsity of the Jacobian of the constraints. The aerodynamics of the spaceplane was computed using an Euler code and the results were used to create a surrogate model based on a non-Stationary Gaussian Process procedure that was specially developed for this study.
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Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-Stationary Gaussian Process
Structural and Multidisciplinary Optimization, 2015Co-Authors: Robin Dufour, Julien Muelenaere, Ali ElhamAbstract:This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization Process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using empirical formulas. The trajectory was optimized using a pseudo-spectral approach with an automated mesh refinement that allowed for increasing the sparsity of the Jacobian of the constraints. The aerodynamics of the spaceplane was computed using an Euler code and the results were used to create a surrogate model based on a non-Stationary Gaussian Process procedure that was specially developed for this study.
Julien Muelenaere - One of the best experts on this subject based on the ideXlab platform.
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Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-Stationary Gaussian Process
Structural and Multidisciplinary Optimization, 2015Co-Authors: Robin Dufour, Julien Muelenaere, Ali ElhamAbstract:This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization Process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using empirical formulas. The trajectory was optimized using a pseudo-spectral approach with an automated mesh refinement that allowed for increasing the sparsity of the Jacobian of the constraints. The aerodynamics of the spaceplane was computed using an Euler code and the results were used to create a surrogate model based on a non-Stationary Gaussian Process procedure that was specially developed for this study.
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Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-Stationary Gaussian Process
Structural and Multidisciplinary Optimization, 2015Co-Authors: Robin Dufour, Julien Muelenaere, Ali ElhamAbstract:This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization Process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using empirical formulas. The trajectory was optimized using a pseudo-spectral approach with an automated mesh refinement that allowed for increasing the sparsity of the Jacobian of the constraints. The aerodynamics of the spaceplane was computed using an Euler code and the results were used to create a surrogate model based on a non-Stationary Gaussian Process procedure that was specially developed for this study.
José R. León - One of the best experts on this subject based on the ideXlab platform.
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Level curves crossings and applications for Gaussian models
Extremes, 2010Co-Authors: Marie F. Kratz, José R. LeónAbstract:Representations into the Itô-Wiener Chaos and asymptotic results such as CLTs are obtained for the curve-crossings number of a Stationary Gaussian Process according to the form of the curve. Applications in physics and sea modelling follow, with the study of the estimator of the natural frequency of a harmonic oscillator and the study of specular points.
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on the second moment of the number of crossings by a Stationary Gaussian Process
arXiv: Probability, 2006Co-Authors: Marie Kratz, José R. LeónAbstract:Cram\'{e}r and Leadbetter introduced in 1967 the sufficient condition \[\frac{r''(s)-r''(0)}{s}\in L^1([0,\delta],dx),\qquad \delta>0,\] to have a finite variance of the number of zeros of a centered Stationary Gaussian Process with twice differentiable covariance function $r$. This condition is known as the Geman condition, since Geman proved in 1972 that it was also a necessary condition. Up to now no such criterion was known for counts of crossings of a level other than the mean. This paper shows that the Geman condition is still sufficient and necessary to have a finite variance of the number of any fixed level crossings. For the generalization to the number of a curve crossings, a condition on the curve has to be added to the Geman condition.
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on the second moment of the number of crossings by a Stationary Gaussian Process
Annals of Probability, 2006Co-Authors: Marie Kratz, José R. LeónAbstract:Cram er and Leadbetter introduced in 1967 the sucien t condition Z 0 r 00 (s) r 00 (0) s ds 0; to have a nite variance of the number of zeros of a centered Stationary Gaussian Process with twice dieren tiable covariance function r. This condition is known as the Geman condition, since Geman proved in 1972 that it was also a necessary condition. Up to now no such criterion was known for counts of crossings of a level other than the mean. This paper shows that the Geman condition is still sucien t and necessary to have a nite variance of the number of any xed level crossings. For the generalization to the number of a curve crossings, a condition on the curve has to be added to the Geman condition.
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On the second moment of the number of crossings by a Stationary Gaussian Process.
Annals of Probability, 2006Co-Authors: Marie Kratz, José R. LeónAbstract:Cramér and Leadbetter introduced in 1967 the sufficient condition [(r''(s)-r''(0))/s ] \in L^1([0,\delta],dx), \delta>0, to have a finite variance of the number of zeros of a centered Stationary Gaussian Process with twice differentiable covariance function r. This condition is known as the Geman condition, since Geman proved in 1972 that it was also a necessary condition. Up to now no such criterion was known for counts of crossings of a level other than the mean. This paper shows that the Geman condition is still sufficient and necessary to have a finite variance of the number of any fixed level crossings. For the generalization to the number of a curve crossings, a condition on the curve has to be added to the Geman condition.
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weak convergence of a nonlinear functional of a Stationary Gaussian Process application to the local time
Acta Mathematica Hungarica, 2000Co-Authors: José R. León, A LeonardAbstract:Let {Xt : 0 ≦ t ≦ 1} be a centered Stationary Gaussian Process, with correlation function satisfying the condition ρ(t) = 1 − tβL(t), 0 0, we study the properties of the Donsker line associated with p-th order variations \(\sum\limits_{i = 1}^{[N{\text{ }}t]} {|X_{i/N} } - {\text{ }}X_{(i - 1)/N} |^p \). We also study the relationship between the number of crossings of a regularization of the initial Process and the local time of the initial Process. The results depend on the values of β.
Kyungjae Lee - One of the best experts on this subject based on the ideXlab platform.
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leveraged non Stationary Gaussian Process regression for autonomous robot navigation
International Conference on Robotics and Automation, 2015Co-Authors: Sungjoon Choi, Eunwoo Kim, Kyungjae LeeAbstract:In this paper, we propose a novel regression method that can incorporate both positive and negative training data into a single regression framework. In detail, a leveraged kernel function for non-Stationary Gaussian Process regression is proposed. With this new kernel function, we can vary the correlation betwen two inputs in both positive and negative directions by adjusting leverage parameters. By using this property, the resulting leveraged non-Stationary Gaussian Process regression can anchor the regressor to the positive data while avoiding the negative data. We first prove the positive semi-definiteness of the leveraged kernel function using Bochner's theorem. Then, we apply the leveraged non-Stationary Gaussian Process regression to a real-time motion control problem. In this case, the positive data refer to what to do and the negative data indicate what not to do. The results show that the controller using both positive and negative data outperforms the controller using positive data only in terms of the collision rate given training sets of the same size.