2. 1 Using Multiple Surrogates for Metamodeling Raphael T. Haftka (and Felipe A. C. Viana University of Florida

3. 2 KEY QUESTIONS Is it possible to choose the best surrogate for a given problem? What are the advantages of using multiple surrogates in optimization? Surrogates are only approximations and as such they incur errors. This raises questions that will be discussed in this lecture:

4. 3 WHY SO MANY SURROGATES? Different statistical models: -Response surface assumes known function and noise in data. -Kriging assumes exact data and random function. Different basis functions (polynomials, radial basis functions). Different loss functions (mostly SVR): -RMS is not sacred (L1 has some advantages). Popular surrogates: polynomial response surface. (PRS) kriging models (KRG), support vector regression (SVR). Each has multiple flavors.

5. 4 WE USE ROOT MEAN SQUARE ERROR Root Mean Square Error ( RMSE ) in design domain with volume V: where is error in prediction of the surrogate model,. Compute RMSE by Monte-Carlo integration at large number of p test test points: Used only for assessing accuracy for test problems.

6. 5 DOE FOR FIT AND DOE FOR INTEGRATION Example of design of experiments (DOE) used for fitting functions of 2 variables Two of the five highly dense DOEs used for RMSE estimation. RMSE is the average of the values obtained with the five DOEs.

7. 6 HOW WE GENERATE A LARGE SET OF SURROGATES A set of 24 basic surrogates is generated by varying the model technique and the respective associated parameters.

8. 7 NO BEST SURROGATE EVEN FOR GIVEN FUNCTION Branin-Hoo function (100 DOEs) 12 Points 20 Points For 11 test problems, 12 surrogates were the best at least 10 times. Every problem had at least 2 surrogates that worked the best at least 10 times.

9. 8 TEST PROBLEMS Other analytical test functions

10. 9 AIRFOIL APPLICATION AIRCRAFT TAKEOFF PERFORMANCE: Requires airfoil lift, drag and pitching moment coefficients. 11 design variables: 10 variables describing the airfoil and one for the angle of attack. 450 simulations: 156 points for fitting and 294 points for RMSE computation. Points selected randomly for 100 DOEs.

11. 10 CROSS-VALIDATION ERRORS One data point is ignored and surrogate fitted to other p – 1 points. Repeat for each data point to obtain the vector of PRESS errors,. For large p, k-fold strategy used instead. Leaves k points out each time. With the PRESS vector, estimate RMSE as: We can now compare surrogates on the basis of their PRESS error.

12. 11 CORRELATION IMPROVES WITH NUMBER OF POINTS CONCLUSION: With enough points (even sparse) can use PRESS RMS to choose a good surrogate. Mean value of the correlation between PRESS RMS and RMSE (out of 100 experiments).

13. 12 FREQUENCY OF BestRMSE vs. BestPRESS For large number of points, the best 3 surrogates according to both RMSE (in blue) and PRESS RMS (in red) tend to be the same.

14. 13 COMPUTATIONAL COST Wall (wait) time on an Intel Core2 T5500 1.66GHz, 2GB or RAM laptop, running MATLAB 7.0 under Windows XP.

15. 14 PASSIVE HELICOPTER VIBRATION REDUCTION In helicopters, the dominant source of vibrations is the rotor (N b /rev). In the passive approach: –Objective function consists of a suitable combination of the N b /rev hub loads –Constraints: stability margin, frequency placement, autorotation, side constraints –Design variables: cross-sectional dimensions, mass and stiffness distributions along the span, pretwist, and geometrical parameters which define advanced geometry tips Aerodynamic environment is expensive to model Glaz, B, Goel, T, Liu, L, Haftka, RT, Friedmann. (2009)“Multiple-Surrogate Approach to Helicopter Rotor Blade Vibration Reduction” AIAA Journal,Vol 47(1), 271–282

16. 15 OPTIMIZATION PROBLEM Objective function to be minimized: – –Weighted sum of the 4/rev oscillatory hub shear resultant and the 4/rev oscillatory hub moment resultant 17 Design Variables: t 1, t 2, t 3, and m ns –Three thickness defined at 0%, 25%, 50%, 75%, and 100% blade stations –Non-structural mass is defined at 68% and 100% stations

17. 16 EXPENSIVE STRESS CONSTRAINT Assuming isotropy, Von Mises’ criterion is used to determine if the blade yields, with a factor of safety. – –Constraint is enforced at a set of discrete points. Calculation of blade stresses is as expensive as a vibration objective function evaluation since a forward flight simulation is needed. A surrogate used for this constraint.

18. 17 Weighted (PWS) Surrogate Construction Optimal Latin hypercubes (OLH) used to create surrogates –Out of a 300 pt. OLH, 283 had converged trim solutions (53 hours) –Out of a 500 pt. OLH, 484 had converged trim solutions (82 hours) Each simulation took 8 hours, with 40-50 run in parallel Fitting plus PRESS took 7-10 minutes for 283 points, 30-40 minutes for 484 Weights in table inversely proportional to PRESS error. Weight Coefficient Sample Size F 4X F 4Y F 4Z M 4X M 4Y M 4Z JStress Constraint w poly 2830.410.400.320.370.310.290.330.35 w krg 2830.480.470.46 0.450.410.460.44 w RBNN 2830.120.130.220.170.250.300.21 w poly 4840.420.440.360.380.340.330.380.40 w krg 4840.450.420.430.450.410.380.42 w RBNN 4840.130.140.220.180.260.290.200.18

19. 18 Errors at 197 test points 283: Kriging has lowest error 484: Polynomials are lowest in some instances 283 Sample Points484 Sample Points Average Errors

20. 19 Optimization Results Among individual approximation methods: –Poly. result in the best design with 283 sample points, and the worst design with 484 sample points –RBNN lead to the best design with 484 sample points Each optimization required 2-4 hours with about 200,000 function evaluations. None of the surrogates led to the same design SurrogateSample Size Vibration Reduction Poly.28364% KRG28354% RBNN28357% Poly.48445% KRG48456% RBNN48468% Vibration reduction (relative to MBB BO-105)

21. 20 CONCLUDING REMARKS The most accurate surrogate for a given function depends on the design of experiments and point density. The cross validation error identifies accurate surrogates well, especially as the number of points in the DOE increases. Cost of fitting multiple surrogates and calculating cross- validation errors low enough to use now for most expensive simulation problems. Optimizing with several surrogates adds little to overall cost, and the best design may be obtained by a less accurate surrogate.