Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

Two Bifurcation Transition Processes in Floating Half Zone Convection of Larger Prandtl Number Fluid

Processes of the onset oscillation in the thermocapillaxy convection under the Earth's gravity are investigated by the numerical simulation and experiments in a floating half zone of large Prandtl number with different volume ratio. Both computational and experimental results show that the steady and axisymmetric convection turns to the oscillatory convection of m=1 for the slender liquid bridge, and to the oscillatory convection before a steady and 3D asymmetric state for the case of a fat liquid bridge. It implies that, there are two critical Marangoni numbers related, respectively, to these two bifurcation transitions for the fat liquid bridge. The computational results agree with the results of ground-based experiments.

The why, the how, and the what of mental processes

Let us ask the following questions concerning any given mental process: (i) which are the reasons for its being, (ii) which are the ostensible characteristics of its evolution, (iii) which is the inner essence of the process, its identity, its suchness, and (iv) which are the relations between such why, how, and what. Sometimes these questions can be answered to our complete satisfaction: when somebody asks “Why did you eat the pie?” and the answer is “Because I was hungry” the exchange can at a neutral level be considered complete. But when one reflects further into the more profound reasons of anything that happens, then it is impossible to say if a good explanation can always be given concerning such why. Obvious answers to daily questions may satisfy our unreflecting curiosity, but not our deeper inquisitiveness.

Influence of stochastic mesoscopic structure on macroscopic mechanical behavior of brittle material

A newly developed numerical code, MFPA(2D) (Material Failure Process Analysis), is applied to study the influence of stochastic mesoscopic structure on macroscopic mechanical behavior of rock-like materials. A set of uniaxial compression tests has been numerically studied with numerical specimens containing pre-existing crack-like flaw. The numerical results reveal the influence of random mesoscopic structure on failure process of brittle material, which indicates that the variation of failure mode is strongly sensitive to the local disorder feature of the specimen. And the patterns of the crack evolution in the specimens are very different from each other due to the random mesoscopic structure in material. The results give a good explanation for various kinds of fracture modes and peak strength variation observed in laboratory studies with specimens made from the same rock block being statistically homogenous in macro scale. In addition, the evolution of crack is more complicated in heterogeneous cases than in homogeneous cases.

Bayesian learning of noisy Markov decision processes

This work addresses the problem of estimating the optimal value function in a Markov Decision Process from observed state-action pairs. We adopt a Bayesian approach to inference, which allows both the model to be estimated and predictions about actions to be made in a unified framework, providing a principled approach to mimicry of a controller on the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from theposterior distribution over the optimal value function. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Statistical and stochastic description of microvoid evolution observed in dynamic failure of ductile materials

A brief review is presented of statistical approaches on microdamage evolution. An experimental study of statistical microdamage evolution in two ductile materials under dynamic loading is carried out. The observation indicates that there are large differences in size and distribution of microvoids between these two materials. With this phenomenon in mind, kinetic equations governing the nucleation and growth of microvoids in nonlinear rate-dependent materials are combined with the balance law of void number to establish statistical differential equations that describe the evolution of microvoids' number density. The theoretical solution provides a reasonable explanation of the experimentally observed phenomenon. The effects of stochastic fluctuation which is influenced by the inhomogeneous microscopic structure of materials are subsequently examined (i.e. stochastic growth model). Based on the stochastic differential equation, a Fokker-Planck equation which governs the evolution of the transition probability is derived. The analytical solution for the transition probability is then obtained and the effects of stochastic fluctuation is discussed. The statistical and stochastic analyses may provide effective approaches to reveal the physics of damage evolution and dynamic failure process in ductile materials.

Progress In Modeling Of Fluid Flows In Crystal Growth Processes

Modeling of fluid flows in crystal growth processes has become an important research area in theoretical and applied mechanics. Most crystal growth processes involve fluid flows, such as flows in the melt, solution or vapor. Theoretical modeling has played an important role in developing technologies used for growing semiconductor crystals for high performance electronic and optoelectronic devices. The application of devices requires large diameter crystals with a high degree of crystallographic perfection, low defect density and uniform dopant distribution. In this article, the flow models developed in modeling of the crystal growth processes such as Czochralski, ammonothermal and physical vapor transport methods are reviewed. In the Czochralski growth modeling, the flow models for thermocapillary flow, turbulent flow and MHD flow have been developed. In the ammonothermal growth modeling, the buoyancy and porous media flow models have been developed based on a single-domain and continuum approach for the composite fluid-porous layer systems. In the physical vapor transport growth modeling, the Stefan flow model has been proposed based on the flow-kinetics theory for the vapor growth. In addition, perspectives for future studies on crystal growth modeling are proposed. (c) 2008 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.

Comparison Of The Quasi-Static Method And The Dynamic Method For Simulating Fracture Processes In Concrete

Concrete is heterogeneous and usually described as a three-phase material, where matrix, aggregate and interface are distinguished. To take this heterogeneity into consideration, the Generalized Beam (GB) lattice model is adopted. The GB lattice model is much more computationally efficient than the beam lattice model. Numerical procedures of both quasi-static method and dynamic method are developed to simulate fracture processes in uniaxial tensile tests conducted on a concrete panel. Cases of different loading rates are compared with the quasi-static case. It is found that the inertia effect due to load increasing becomes less important and can be ignored with the loading rate decreasing, but the inertia effect due to unstable crack propagation remains considerable no matter how low the loading rate is. Therefore, an unrealistic result will be obtained if a fracture process including unstable cracking is simulated by the quasi-static procedure.