Identification of Physical Parameters Using Change-Point Kernels
2
0
0
Texte intégral
(2)
(3)
(4)
(5)
(6)
(7)
(8) . an author's
(9)
(10)
(11) https://oatao.univ-toulouse.fr/18005
(12)
(13) .
(14) . Chiplunkar, Ankit and Rachelson, Emmanuel and Colombo, Michele and Morlier, Joseph Identification of Physical Parameters Using Change-Point Kernels. (2016) In: SIAM Uncertainty Quantification 2016, 5 April 2016 - 8 April 2016 (Lausanne, Switzerland).. .
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23) .
(24) SIAM UQ 2016. April 2016. Identification of Physical Parameters Using Change-Point Kernels Ankit Chiplunkar (ankit.chiplunkar@airbus.com) Emmanuel Rachelson, Michele Colombo, Joseph Morlier. 1. Context 1.. 3. Gaussian Process Regression. Engineering design is a costly exercise, primarily because. 1.. Gaussian Process is a flexible, non-linear, prior over functions.. gathering data for various design cases requires constructing. 2.. It enables to tractably compute the posterior distribution which is. and experimenting on that design point. 2.. 5. Results and Discussion. consistent with prior belief and observed data.. Hence engineers learn basic principles of the system and. 3.. construct more detailed models based on the initial principles. The prior can be easily manipulated to encode a hypothesis space or family of functions.. and simplifying assumptions. Eg. FEM in structural design or. Likelihood. Prior. CFD in fluid simulations.. Posterior 5.1 Gaussian Process regression using a SE kernel for XFLR5 airfoil. 5.2 Regression using a Linear to SE changepoint kernel. State of art method 38,4 GPa Young’s modulus Changepoint 38,6+- 2,3 GPa Young’s Modulus. 1.1 Stress-strain diagram for Aluminum AL6061 alloy.. 1.2 FEM of a small satellite, elements represent AL6061.. 1.3 Displacements of the satellite under stress.. GP m(x), 𝐤 𝐱, 𝐱 ′ 3.1 Random draws from a Standard Exponential (SE) prior.. 𝟏 𝑻 −𝟏 𝒚 𝑲 𝒚 𝟐. Various physical systems are approximated by a linear domain. 2.. 1.. 2.. for tensile test of AL6061 and lift diagrams for XFLR5 airfoil will. 0,94% of strain. Marginal likelihood 372.3145 +-5,57. 1.. The marginal likelihood of CP(linear, SE) kernel is rigged with. We will use a changepoint kernel to encode the approximation of. many local minima's. The algorithm tries to put a changepoint at. change from a linear to non-linear domain.. every observation point. Hence either using a constrained. Changepoint kernels can be defined through multiplication with. optimization or a global optimizer is advised. 2.. sigmoidal functions. 3.. We want to estimate the physical parameters, experimental data. ChangePoint. GP(𝒌𝑲−𝟏 𝒚, 𝒌(∗,∗) − 𝒌𝑲−𝟏 𝒌) 3.3 Posterior eliminates all functions non-coherent with data. 4. Method. linear domain are important inputs in subsequent models. eg. Young’s modulus and Plasticity. 𝑛 2. 𝑲 − log 2𝜋. 5.1 GP regression for AL6061 data, using the changepoint kernel. The Young’s modulus calculated using engineering judgement was 38,4 GPa. near the equilibrium and a non-linear domain where the. approximations wear off. The values of slope and position of. 𝟏 𝒍𝒐𝒈 𝟐. 3.2 Mean values from three different priors, maximizing likelihood gives best prior.. 2. Objective 1.. −. cross-validating the learners improves performance.. We maximize marginal likelihood to estimate the position of the 3.. changepoint.. The changepoint kernel is also numerically unstable hence. 𝐾𝑐𝑝 𝑘1 , 𝑘2 = 𝜎 𝑥 𝑘1 𝑥, 𝑥 ′ 𝜎 𝑥 ′ + 1 − 𝜎 𝑥 𝑘2 𝑥, 𝑥 ′ 1 − 𝜎 𝑥 ′. Osborne et al perform changepoint estimation by placing a prior. over them and performing a MAP estimate. We would like use this method in the future for changepoint estimation.. be used to validate our method.. 6. References 1. 2. 3. 2.1 Tensile test setup for stress strain diagrams.. 2.2 Flow over an airfoil, the lift depends on the angle of air. © AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.. 4.1 Random draws from Linear to Periodic changepoint kernel. 4.2 Random draws from Linear to SE changepoint kernel. 4.. Garnett, R., Osborne, M. A., & Roberts, S. J. (2009). Sequential Bayesian prediction in the presence of changepoints. Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning (2005). Abbott, Ira H., Albert E. Von Doenhoff, and Louis Stivers Jr. "Summary of airfoil data." (1945). Jhbdel, Airfoil flow, 2010.
(25)
Documents relatifs
Population qui croit aussi le plus fortement en Europe, la France ne compte pas encore dans le monde de la finance islamique, sauf quelques banques qui sont actives sur ce
The used dynamic characteristics to locate and quantify structural damages are natural frequencies, mode shapes, mode shape curvature, modal strain energy,
Compared to zero or one dimensional nanostructures, the synthesis of two-dimensional nanostructures in high yield has always been more involved, often requiring complex and
Abstract : The aims of this work is the modeling of the hysteresis loop in ferromagnetic materials, and allowed to highlight of the difficulty that exists in the choice of an
The main question of the structural and practical identifiability analysis can then be formulated as follows (Dochain et al., 1995): ’Assume that a certain number of state variables
The fitness function for a particle is defined as the squared error between the measured values and the calculated ones (ob- tained by considering the associated position) of a
Bien d’autres événements concernent encore Galilée, comme l’énoncé du principe d’inertie, qu’il concevait comme valide seulement pour la mécanique terrestre (sur le
The optimal coefficients of BP∆Σ structure are achieved by minimization of a quadratic criterion based on prediction error between desired digital filter and noise transfer model..
