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svm@student.ethz.ch
Possible System Identification Approaches
PhD Student, Seyed Vahid Moosavi
Professor Ludger Hovestadt
...
What is System Identification?
Predict the “Smoke Amount”
based on “the building features”
Bridge Displacement
Wind Flow
S...
Why System Identification?
• System-State Space Modeling
• Sensitivity analysis
• What-if analysis
• System Design Optimiz...
System Identification In a Symbolic Form
System
(black-Box??)
Or
White box
Model Input
(e.g. load cases)
Some real
Phenome...
Classic System Identification
(We can easily measure any variable of interest)
5
And If we can’t measure enough or we want to Design a new
system….
We Build a Simulation Model
6
Goulet, J.-A., Kripakaran, P., and Smith, I.F.C. (2010). Multimodel structural performance monitoring. Journal of Structur...
So…
A set Partial
Differential
Equations
(All possible instances of
comprehensive models)
Finite Element Analysis
Approxim...
A sample result
Goulet, J.-A., Kripakaran, P., and Smith, I.F.C. (2010). Multimodel structural performance monitoring. Jou...
But…
• Normally these simulations are time consuming.
no. of Parameters Np 5 10
No. of possible
values for each
parameter
...
What can we do?
What is the State of the art?
(Generalize It!!)
Surrogate Models
Meta-modeling
Response Surface Method
• A...
First step results… ( A sample case: Data and explanation by James A. Gulet)
Parameters:
1. Plymouth-side support longitud...
Sample data
Candidate
Rejected
• Classic approach is “Time Consuming” even for simplified models
• We used a sample of mod...
Map interpretation
Normalized
Values of each
parameter
Each map is representing the value of one parameter in our FE model...
The effect of first Parameter
15
The effect of second Parameter
16
The effect of third Parameter
17
The effect of fourth Parameter
18
The effect of fifth Parameter
19
What we got so far
• Faster Model Tuning and sensitivity analysis
• Finding the most important parameters (so lower requir...
Thanks!
21
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System identification and Surrogate Modeling

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June-2012 final

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System identification and Surrogate Modeling

  1. 1. svm@student.ethz.ch Possible System Identification Approaches PhD Student, Seyed Vahid Moosavi Professor Ludger Hovestadt 14 June 2012 1
  2. 2. What is System Identification? Predict the “Smoke Amount” based on “the building features” Bridge Displacement Wind Flow Stock Market 2
  3. 3. Why System Identification? • System-State Space Modeling • Sensitivity analysis • What-if analysis • System Design Optimization 3
  4. 4. System Identification In a Symbolic Form System (black-Box??) Or White box Model Input (e.g. load cases) Some real Phenomenon (Model Parameters or structure) (e.g. strain or tension parameters) Some real observations (e.g. test cases) Model Output (predicted) (e.g. vertical displacement of the bridge) Error Threshold (or confidence interval) 4
  5. 5. Classic System Identification (We can easily measure any variable of interest) 5
  6. 6. And If we can’t measure enough or we want to Design a new system…. We Build a Simulation Model 6
  7. 7. Goulet, J.-A., Kripakaran, P., and Smith, I.F.C. (2010). Multimodel structural performance monitoring. Journal of Structural Engineering, 136(10):13091318. An Example 7
  8. 8. So… A set Partial Differential Equations (All possible instances of comprehensive models) Finite Element Analysis Approximated by A software e.g. Ansys We have AND considerable Amount of Parameters as INPUTs for FEA Through Exhaustive search 8
  9. 9. A sample result Goulet, J.-A., Kripakaran, P., and Smith, I.F.C. (2010). Multimodel structural performance monitoring. Journal of Structural Engineering, 136(10):13091318. 9
  10. 10. But… • Normally these simulations are time consuming. no. of Parameters Np 5 10 No. of possible values for each parameter Nc 5 5 Possible models Nmodels= Nc^Np 5^5=3125 5^10=9,765,625 Total required time (Nmodels) X (time_per_model ) 3125 X 10= 31250 mins =520 Hours 21 Days 162,7604 H 67,816 Days 10
  11. 11. What can we do? What is the State of the art? (Generalize It!!) Surrogate Models Meta-modeling Response Surface Method • Approximate the input-output of Comprehensive Model (e.g. FE) with a faster approximation using “Statistical approaches”. Toward a Black-Box Method 11
  12. 12. First step results… ( A sample case: Data and explanation by James A. Gulet) Parameters: 1. Plymouth-side support longitudinal stiffness 1E [4, 11] kN/mm 2. Saltash-side support longitudinal stiffness 1E [4, 11] kN/mm 3. Deck expansion joint longitudinal stiffness 1E [4, 11] kN/mm 4. Main-cable initial strain [5E-4, 3E-3] mm/mm 5. Sidespan cable initial strains [5E-4, 3E-3] mm/mm. The interval of each parameter value is discretized in five parts to generate a hyper-grid containing 3125 (5^5) combination of parameters. The result of this process is an initial model set containing the predicted frequencies and mode shapes for all 3125 model instances. 12
  13. 13. Sample data Candidate Rejected • Classic approach is “Time Consuming” even for simplified models • We used a sample of models from FE simulation with their final results. • We trained a Self Organizing Map (SOM) to see the relation bet. Different parameters values and the result of the FE model 13
  14. 14. Map interpretation Normalized Values of each parameter Each map is representing the value of one parameter in our FE model Each dot in the map shows one possible model The labels of each dot is either 0 (rejected) or 1 (accepted) 14
  15. 15. The effect of first Parameter 15
  16. 16. The effect of second Parameter 16
  17. 17. The effect of third Parameter 17
  18. 18. The effect of fourth Parameter 18
  19. 19. The effect of fifth Parameter 19
  20. 20. What we got so far • Faster Model Tuning and sensitivity analysis • Finding the most important parameters (so lower required time for model generation) • We somehow generalized the behavior of FE models • We can conduct modified sampling method And Next possible steps Focusing on Surrogate Models for fast Statistical Models And Applications in Design-Optimization 20
  21. 21. Thanks! 21

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