Bayesian updating of uncertanties in the description of heat and moisture transport in heterogenous materials

The description of heterogenous material is here given within the probabilistic framework, where uncertain material properties in time and/or space are represented by stochastic processes and fields. For material with uncertain structure such as quarry, masonry etc., we study the coupled heat and moisture trasnport modelled by the Künzel equations. The transport coefficients defining the material behavior are nonlinear functions of structural responses - the temperature and moisture fields - and material properties. In order to closely determine the mentioned parameters of such system we focus our attention on the solution of inverse problem via direct, non-sampling Bayesian update methods which combine the a priori information with the measurment data for the description of the posterior distribution of parameters. Namely, we consider material parameters, observations and forward operator as random. Since the measurments are always polluted by some kind of measurment error we modell it here by a Gaussian distribution. The new approach has shown to be effective and reliable in comparison to most methods, which take the form of integrals over the posterior and compute them by sampling, e.g. Markov chain Monte Carlo (MCMC). In addition, we compare our method with this and other Bayesian update methods.