Tsilifis, P.; Pandita, P.; Ghosh, S.; Wang, L. Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds. Entropy2022, 24, 1291.
Tsilifis, P.; Pandita, P.; Ghosh, S.; Wang, L. Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds. Entropy 2022, 24, 1291.
Tsilifis, P.; Pandita, P.; Ghosh, S.; Wang, L. Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds. Entropy2022, 24, 1291.
Tsilifis, P.; Pandita, P.; Ghosh, S.; Wang, L. Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds. Entropy 2022, 24, 1291.
Abstract
Bayesian techniques for engineering problems, that rely on Gaussian process (GP) regression, are known for their ability to quantify epistemic and aleatory uncertainties and for being data efficient. The mathematical elegance of applying these methods usually comes at a high computational cost when compared to deterministic and empirical Bayesian methods. Furthermore, using these methods becomes practically infeasible in scenarios characterized by a large number of inputs and thousands of training data. The focus of this work is on enhancing Gaussian Process-based metamodeling and model calibration tasks, when the size of the training datasets is significantly large. To achieve this goal, we employ a stochastic variational inference algorithm that enables rapid statistical learning of the calibration parameters and hyperparameter tuning, while retaining the rigor of Bayesian inference. The numerical performance of the algorithm is demonstrated on multiple metamodeling and model calibration problems with thousands of training data.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
