FULL BOOK

• • Chapter 1. Introduction to Probabilistic Methods & Uncertainty Modeling in Direct & Inverse Problems
  • Chapter 2. Application of Data Science in the Sustainable Energy Industry
  • Chapter 3. Overview of Classic Surrogates and Metamodeling Strategies in Structural Analysis
  • Chapter 4. Uncertainty Quantification in Inverse Kinematics Computation of Space Robots by NeuralNetworks
  • Chapter 5. Metamodel-Based Simulation Optimization: A Systematic Literature Review
  • Chapter 6. Principal Component Analysis: An Overview and Applications in Multivariate Engineering Problems
  • Chapter 7. Challenges for Structural Health Monitoring: Nonlinearities and Uncertainties
  • Chapter 8. Sensor Placement Optimization of a Helicopter Main Rotor Blade
  • Chapter 9. Challenges for Structural Health Monitoring: Nonlinearities and Uncertainties
  • Chapter 10. Applications of Numerical and Statistical Methods for Monitoring and Control Engineering Variables in a Concrete Dam
  • Chapter 11. Application of Statistical Processing Techniques to the Impedance-based SHM for the Oil& Gas Industry
  • Chapter 12. On Error Estimators and Bayesian Approaches in Computational Model Validation
    Chapter 13. On Kriging Techniques & Impedance-based SHM as Applied to Damage Detection in 2D Structures
  • Chapter 14. Time Series Models and Expansion Optimal Linear Estimation for Time-DependentReliability Analysis
  • Chapter 15. Bayesian Optimal Experimental Design
  • Chapter 16. Airplane Optimal Robust Design Combined with an Entropy-Statistics UnsupervisedLearning Classification Algorithm
  • Chapter 17. On Error Estimators and Bayesian Approaches in Computational Model Validation
  • Chapter 18. Random Fatigue Under Uniaxial and Multiaxial Loading Conditions
  • Chapter 19. On Error Estimators and Bayesian Approaches in Computational Model Validation