## Inverse Analyses with Model Reduction: Proper Orthogonal Decomposition in Structural MechanicsIn this self-consistent monograph, the author gathers and describes different mathematical techniques and combines all together to form practical procedures for the inverse analyses. It puts together topics coming from mathematical programming, with soft computing and Proper Orthogonal Decomposition, in order to show, in the context of structural analyses, how the things work and what are the main problems one needs to tackle. Throughout the book a number of examples and exercises are worked out in order to make reader practically familiar with discussed topics. |

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### Contents

1 | |

Optimization Algorithms | 19 |

Proper Orthogonal Decomposition and Radial Basis Functions for Fast Simulations | 85 |

Inverse Analyses in Structural Problems Putting All the Pieces Together | 140 |

### Other editions - View all

Inverse Analyses with Model Reduction: Proper Orthogonal Decomposition in ... Vladimir Buljak No preview available - 2014 |

Inverse Analyses with Model Reduction: Proper Orthogonal Decomposition in ... Vladimir Buljak No preview available - 2011 |

### Common terms and phrases

ABAQUS approach approximation ASCII Cauchy point coefficients components computed constrain convergence correlation curve defined derivatives dimensionality discrepancy function dog-leg eigenvalues eigenvectors end end equation error example experimental FE model FE simulations given grad guess HESS Hessian matrix implementation initial input file instance=plate-1 interpolation inverse analyses procedure inverse problem itiner iter k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k Lagrange multipliers large number line search MATLAB code MATLAB function MAXIT model function Newton direction nodes numerical model objective function optimization algorithms optimization problem parameter identification problems performed positive definite previously Proper Orthogonal Decomposition pseudo-experimental data ratio reduced represents routine snapshot matrix solution solved sought parameters steepest descend step length structural sub-problem system response total number trust region trust region algorithm vector visualized in Fig Young modulus