Parameter identification and uncertainty quantification in stochastic state space models and its application to texture analysis

In this paper, a computational framework, which enables efficient and robust parameter identification, as well as uncertainty quantification in state space models based on Ito stochastic processes, is presented. For optimization, a Maximum Likelihood approach based on the system's corresponding Fokker-Planck equation is followed. Gradient information is included by means of an adjoint approach, which is based on the Lagrangian of the optimization problem. To quantify the uncertainty of the Maximum-A-Posteriori estimates of the model parameters, a Bayesian inference approach based on Markov Chain Monte Carlo simulations, as well as profile likelihoods are implemented and compared in terms of runtime and accuracy. The framework is applied to experimental electron backscatter diffraction data of a fatigued metal film, where the aim is to develop a model, which consistently and physically meaningfully captures the metal's microstructural changes that are caused by external loading. (C) 2019 IMACS. Published by Elsevier B.V. All rights reserved.