MITESD_77S10_lec17

MITESD_77S10_lec17 - Multidisciplinary System Design...

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Unformatted text preview: Multidisciplinary System Design Optimization (MSDO) Approximation Methods Karen Willcox Slides from: Theresa Robinson, Andrew March 1 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Outline • Introduction to approximation methods • Data fit methods – Polynomial response surfaces – Kriging • Model order reduction – Reduced-basis methods – Proper orthogonal decomposition • Multifidelity methods – Trust-region model management 2 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Approximation Methods • Replace the simulation with an approximation or “surrogate” • Uses some data from the initial simulation – Can be global or local • Surrogate is much less computationally expensive to evaluate • Not just optimization – Uncertainty Quantification (e.g. Monte Carlo simulation methods) – Visualization 3 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Why Approximation Methods We have seen throughout the course the constant trade-off between computational cost and fidelity. intermediate fidelity (e.g. vortex lattice, beam theory) empirical models can we do better? how to implement? Fidelity Level high fidelity (e.g. CFD,FEM) from Giesing, 1998 Level of MSDO trade studies limited optimization/iteration can the results be believed? full MDO Approximation methods provide a way to get high-fidelity model information throughout the optimization without the computational expense. 4 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Data Fit Methods • Sample the simulation at some number of design points – Use DOE methods, e.g. Latin hypercube, to select the points • Fit a surrogate model using the sampled information • Surrogate may be global (e.g., quadratic response surface) or local (e.g., Kriging interpolation) • Surrogate may be updated adaptively by adding sample points based on surrogate performance (e.g., EGO) 5 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Polynomial Response Surface Method • Surrogate model is a local or global polynomial model • Can be of any order – Most often quadratic; higher order requires many samples • Advantages: Simple to implement, visualize, and understand, easy to find the optimum of the response surface • Disadvantages: May be too simple, doesn’t capture multimodal functions well 6 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Global Polynomial Response Surface • Fit objective function with a polynomial • e.g. quadratic approximation: J (x) a0 cii xi2 bi xi i i cij xi x j i,j i • Update model by including a new function evaluation then doing least squares fit to compute the new coefficients 7 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Global Polynomial Response Surface Estimation problem: J J 1 J 2 J J Xc M J=vector containing M responses c0 c T 1 1 x1 1 1 x2 11 x1 x1 2 1 x x2 is the value of 1 x1 for the 2nd sample M x1 Least squares solution: c 8 cp 1 c=vector containing p coefficients X 1 c1 M x2 MM x1 x1 T XX 1 X=M p matrix Each row corresponds to one data sample; each column corresponds to an unknown coefficient XTJ © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Kriging • Adopted from the geostatistics literature • Based on Gaussian process models • Assumes that the output function values are correlated in design space, i.e. closer points are more highly correlated • Can have multiple extrema • Interpolating method – Exact at sample points • Gives estimate of mean squared error – Can use to give error bounds – Can use to choose new sample points 9 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Kriging: Mathematical Background • We want to make a prediction of y at a point x • Uncertain of value: model as a random variable, normally distributed with mean and variance 2 • Consider two points xi and xj • Expect values to be close if the distance between them is small • Formalize this idea by setting: pi n Corr[Y ( x j ), Y ( xk )] exp i x ji xki i1 10 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Kriging Basis Functions pi n Corr[Y ( x j ), Y ( xk )] exp i x ji xki Correlation Correlation i1 Distance between points Each pi and 11 i Distance between points is chosen to best fit the data © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Kriging Kriging Mathematics Cont. • Choose μ, pi, and θi to maximize the likelihood of observing the data – Detailed equations in Giunta and Watson (1998), derivation in Jones (2001) • Kriging predictor is predictor is ⎛n ˆ y x* = μ + ∑ ci exp⎜ − ∑ θ j x* − x i ⎜ i =1 ⎝ j =1 () k mean surface 12 pj ⎞ ⎟ ⎟ ⎠ weighted sum of Gaussians, each centered at a sample point © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Kriging Extensions • Can combine polynomial RSM and Kriging – Apply Kriging to difference between sample values and polynomial approximation • Soft Kriging allows upper and lower bounds, prior CDFs • Efficient Global Optimization (EGO) – Uses Kriging to find “expected improvement” – Samples the point with the largest expected improvement and adds it to the sample set 13 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Efficient Efficient Global Optimization • Jones 1998; based on probability theory • Assumes: f ( x ) ≈ β T x + N (μ ( x ), σ 2 ( x ) ) • β Tx • ( ) is normally regression model : regression term regression term N μ (x), σ 2 (x) : error from distributed, with mean μ(x) and variance σ2(x) • Estimate function values with a Kriging model – Predicts mean and variance Surrogate model is updated model is updated adaptively; kth surrogate is m k (x ) = ¹ (x ) + ¯ T x • Evaluate function at “maximum expected function at maximum expected improvement location(s)” and update model Bayesian Model Calibration f high (x) mk (x) f low (x) • Model the error between a high-fidelity and a low-fidelity function [Kennedy2000, 2001; Huang2006] • If the low-fidelity function is “good”, converges faster • Global calibration procedure 15 k (x) Comparison of Data Fit Methods 16 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Reduced-Basis Methods Consider r feasible design vectors: x1, x2, ..., xr We could consider the desired design to be a linear combination of these basis vectors: r x* x i x C i1 scalar coefficient 17 basis vector added for generality © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Reduced-Basis Methods We can now optimize J(x) by finding the optimal values for the coefficients i. dimension n dimension r 18 • Do one full-order evaluation of resulting answer • Approach is efficient if r << n • Will give the true optimum only if x* lies in the span of {xi} • Basis vectors could be – previous designs – solutions over a particular range (DoE) – derived in some other way (e.g., proper orthogonal decomposition) © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Reduced-Basis Example Example using a reduced-basis approach (van der Plaats Fig 7-2): airfoil design for a unique application. • Many airfoil shapes with known performance are available • Design variables are (x,y) coordinates at chordwise locations (n~100) • Use four basis airfoil shapes (low-speed airfoils) which contain the n geometry points • Plus two basis shapes which allow trailing edge thickness to vary • r=6 (r<<n) • Optimize for high speed, maximum lift with a constraint on drag 19 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Reduced-Basis Example Airfoil basic shapes. From Vanderplaats Figs. 7-2 and 7-3, pg. 260 NACA 2412 Basic shape 1 NACA 64, -412 Basic shape 2 NACA 65, -415 Basic shape 3 Basis functions NACA 65, A215 Basic shape 4 Basic shape 5 y--x/c y--0 y--0 Basic shape 6 y---x/c result of optimization using reduced basis Minimum-drag airfoil. Image by MIT OpenCourseWare. 20 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Proper Orthogonal Decomposition (aka Karhunen-Loève expansions, Principal Components Analysis, Empirical Orthogonal Eigenfunctions, …) Consider K snapshots (solutions at selected times or parameter values) Form the snapshot matrix Choose the n basis vectors to be left singular vectors of the snapshot matrix, with singular values This is the optimal projection in a least squares sense: 21 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Multifidelity Methods • Sometimes there is more than one model for the same system – e.g. Navier Stokes and thin-airfoil theory for wing design, finite-element and beam theory for structural design • Low-fidelity model may provide good information over a wide range, at much lower computational cost • Would like to find optimum of high-fidelity problem, but use low-fidelity model most of the time 22 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics A Hierarchy of Models Images of Figure 1b, 4, and 8 removed due to copyright restrictions. Figures from: Choi, S, Alonso, JJ, Kim, S., Kroo, IM. Two-level multi-fidelity design optimization studies for supersonic jets. 43th AIAA Aerospace Sciences Meeting & Exhibit. January 2005. Image of Low-fidelity EM and High fidelity EM models removed due to copyright restrictions. 23 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Trust-Region Model Management • A rigorous method for determining when to use high-fidelity function calls • Solves a series of subproblems: Minimize ˆ k ( x) J Subject to ˆ k ( x) 0 g xx k c k Several methods exist to handle the approximation of constraints. k c x : center point of trust region at iteration k k 24 : size of trust region at iteration k © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics TrustTrust-Region Model Management • Size of trust region updated depending on how well surrogate predicts high-fidelity function value • Merit function function Γ[J (x ), g (x )] • Ratio of actual to predicted improvement: () () () () Γ x −Γ x ρ= ˆ ˆ Γ x −Γ x k 25 k c k c k * k * © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Trust-Region Model Management • Trust region size update rules: k 0 0 k 0.1 k 0.75 26 Reject step 0.1 0.75 k k1 Accept step Accept step Accept step 0.5 k k1 0.5 k k1 k k1 2 k © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Trust-Region Demonstration 27 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Trust-Region Model Management • Calls high-fidelity analysis once per iteration • Calls surrogate analysis many times per iteration • Provably convergent to local minimum of high fidelity function if surrogate is first-order accurate at center of trust region ˆ • Extensions to the case of x x in Robinson et al. (2008). • Derivative-free approaches in Conn et al. (2009) 28 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Corrections • Include corrections in order to enforce consistency and gain provable convergence of trust-region approach • Additive Correction: ˆ J ( x) J lo (x) ( x) • Multiplicative Correction: ˆ J ( x) surrogate model 29 J lo (x) (x) low-fidelity model © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Multifidelity Optimization • Combines several elements: – Trust regions – Bayesian model calibration – Adaptive sampling – Surrogate models (e.g., interpolation models using Kriging) – Estimation theory • Active area of research 30 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics Combining Estimates of Multifidelity Models • Use Kalman filtering approach to compute combine estimate • Maximum likelihood estimate weights each model according to its variance (pay more attention to models in which we have more confidence) 2 2 ¾ow (x) ¾ ed (x) l m ¹ est (x) = ¹ med (x) 2 + ¹ low (x) 2 2 (x) 2 ¾ow (x) + ¾ ed ¾ow (x) + ¾ ed (x) l m l m 1 2 ¾est (x) = 1 2 ¾low (x) + 1 2 ¾med (x) : • Extends naturally to case with more than two models; much more efficient than nesting (March 2010) Image by MIT OpenCourseWare. Lecture Summary • A number of ways to create approximations, or surrogates • Each has its own area of application, advantages, and disadvantages • Data fit surrogates – Polynomial response surfaces – Kriging • Model order reduction – Reduced basis – Proper orthogonal decomposition • Multifidelity methods 32 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Engineering Systems Division and Dept. of Aeronautics and Astronautics References Alexandrov, N., Dennis, J.E., Lewis, R.M. and Torczon, V., “A trust region framework for managing the use of approximation models in optimization”, NASA CR-201745, ICASE Report No. 97-50, October 1997. Barthelemy, J-F. M. and Haftka, R.T., “Approximation concepts for optimum structural design – a review”, Structural Optimization, 5:129-144, 1993. Conn, A.R., Scheinberg, K. and Vicente, L., “Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points,” SIAM Journal of Optimization, Vol. 20, No.1, pp. 387-415, 2009. Gill, P.E., Murray,W. and Wright, M.H., Practical Optimization, Academic Press, 1986. Giunta, A.A. and Watson, L.T.,”A comparison of approximation modeling techniques: polynomial versus interpolating models”, AIAA Paper 98-4758, 1998. Jones, D.R., “A taxonomy of global optimization methods based on response surfaces,” Journal of Global Optimization, 21, 345-383, 2001. LeGresley, P.A. and Alonso, J.J., “Airfoil design optimization using reduced order models based on proper orthogonal decomposition”, AIAA Paper 2000-2545, 2000. March, A. and Willcox, K., “A Provably Convergent Multifidelity Optimization Algorithm not Requiring High-Fidelity Derivatives,” AIAA-2010-2912, presented at 3rd MDO Specialist Conference, Orlando, FL, April 12-15, 2010. Robinson, T., Willcox, K., Eldred, M., and Haimes, R. “Multifidelity Optimization for Variable Complexity Design,” AIAA Journal, Vol.46, No.11, pp. 2814-2822, 2008. Vanderplaats, G.N., Numerical Optimization Techniques for Engineering Design, Vanderplaats 33 R&D, 1999. MIT OpenCourseWare http://ocw.mit.edu ESD.77 / 16.888 Multidisciplinary System Design Optimization Spring 2010 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. ...
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