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revised_slides_subrata_version_5

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revised_slides_subrata_version_5

  1. 1. © 2015 Bentley Systems, Incorporated Applying Deep Learning to Finite Element Model Calibration Research Intern: Subrata Saha Advisor: Zheng Yi Wu
  2. 2. 2 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Needs for Model Calibration • Adequately represent conditions of in-service infrastructures – Above- and underground infrastructure systems • Assess infrastructure performance – Functionality, capacity, serviceability, safety and deficiency etc. • Support decision-making for proactive maintenance – Be preventive instead of reactive
  3. 3. 3 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated What is it? • Start with initial model, e.g. design model • Adjust model parameters to minimize goodness-of-fit score • Challenges: intensive computations FE Solver Adjust parameters Goodness-of-fit score Stop?Initial model Measured responses Calibrated model
  4. 4. 4 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Apply Surrogate Model • Construct a meta-model (approximation), e.g. deep learning, or CMS • Replace FE full analysis to Improve iterative calibration FE Solver Adjust parameters Goodness-of-fit score Stop?Initial model Measured responses Calibrated modelSurrogate solver
  5. 5. 5 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Apply Deep Learning Solution Input parameters FE Model Solver Goodness-of-fit Input parameters Prediction (DBN DLL) Goodness-of-fit Training Dataset DBN Training Trained Model
  6. 6. 6 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated (1) UCLA factor building model (2) 60 decision variables - 30 for elasticity - 30 for stiffness (3) 1357 beams (4) 30 groups Dataset
  7. 7. 7 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Command Combined Output Training Set Generation • Input variables: – 60 • Output: – Goodness-of-fit score • Training dataset – 6,000 – 12 hours 1. Generate random number using uniform distribution within a certain range for each of the decision variables 2. Compute score using FE solver
  8. 8. 8 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated DBN Prediction Accuracy 10 15 20 25 30 35 40 Score Training samples Training Prediction: UCLA FEM Solver score prediction Target Predicted
  9. 9. 9 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated DBN Prediction Accuracy 10 15 20 25 30 35 40 Score Training samples Prediction: UCLA FEM Solver score prediction Input: hull dimensions and the boat velocity 10 15 20 25 30 35 40 Score Training Samples Training Prediction: UCLA FEM Solver score prediction Target Predicted
  10. 10. 10 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated DBN Prediction Accuracy 0 5 10 15 20 25 30 35 40 45 Score Test Samples Prediction Prediction: UCLA FEM Solver score prediction Predicted Target
  11. 11. 11 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Comparison between FEM and DBN Calibrator RMS error: 0.304611778 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1 6 11 16 21 26 31 36 41 46 51 56 Value Variable FEM Calibrator DBN Calibrator 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 6 11 16 21 26 31 36 41 46 51 56 Error Variable DBN Calibration
  12. 12. 12 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Scores from FEM and DBN Calibrator given top solution 10.63 13.43 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Top Solution ObjectiveValue FEM Calibrator DBN Calibrator DBN Calibration
  13. 13. 13 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated DBN Tune Calibration RMS error: 0.306776256 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1 7 12 17 22 27 32 37 42 47 52 57 Value Variable FEM Calibrator DBN Tune Calibrator 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 6 11 16 21 26 31 36 41 46 51 56 Error Variable Comparison between FEM and DBN Tune Calibrator
  14. 14. 14 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Scores from FEM and DBN Tune given top solution 10.63 11.46 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Top Solution ObjectiveValue FEM Calibrator DBN Tune Calibrator DBN Tune Calibration
  15. 15. 15 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Calibration Time 0 5 10 15 20 25 30 35 40 45 50 FEM Calibrator DBN Tune Calibrator Calibrationtime,hour FEM Calibrator: 48 hours DBN Tune Calibrator: 15 hours Training Dataset Generation: 12 hours DBN Training: 15 minutes DBN Predict: 45 minutes DBN Tune: 2 hours
  16. 16. 16 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Trial vs Fitness Score 10 10.5 11 11.5 12 12.5 13 13.5 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 FitnessValue No. of Trials FEM Calibrator DBN Tune Calibrator
  17. 17. 17 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Trial vs Time 0 5 10 15 20 25 30 35 40 45 50 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 Time,hour No. of Trials FEM Calibrator DBN Tune Calibrator
  18. 18. 18 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Software
  19. 19. 19 | WWW.BENTLEY.COM | © 2015 Bentley Systems, Incorporated Thank You!

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