Statistical properties of a linear stochastic system

In this paper a random linear system of the form of y(t; ω = ∫t∁K(t, τ; ω)x(τ; ω)dr is studied, where the kernel is a stochastic process defined on a probability space. The concept of the modified characteristic function for the output process is introduced. These characteristic functions are used to identify the distribution of the output process over certain subsets of the probability space, Ω, in order to study the statistical properties of the process. Several examples are given to illustrate the usefulness of the resulting theory. These results extend the previous theory of random linear systems, in that until now, the kernel was deterministic in nature

Learning and Designing Stochastic Processes from Logical Constraints

Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics must be known exactly. As this is seldom the case, many methods have been devised over the last decade to infer (learn) such parameters from observations of the state of the system. In this paper, we depart from this approach by assuming that our observations are {\it qualitative} properties encoded as satisfaction of linear temporal logic formulae, as opposed to quantitative observations of the state of the system. An important feature of this approach is that it unifies naturally the system identification and the system design problems, where the properties, instead of observations, represent requirements to be satisfied. We develop a principled statistical estimation procedure based on maximising the likelihood of the system's parameters, using recent ideas from statistical machine learning. We demonstrate the efficacy and broad applicability of our method on a range of simple but non-trivial examples, including rumour spreading in social networks and hybrid models of gene regulation

Derivation of equivalent linear properties of Bouc-Wen hysteretic systems for seismic response spectrum analysis via statistical linearization

A newly proposed statistical linearization based formulation is used to derive effective linear properties (ELPs), namely damping ratio and natural frequency, for stochastically excited hysteretic oscillatorsinvolving the Bouc-Wen force-deformation phenomenological model. This is achieved by first using a frequency domain statistical linearization step to substitute a Bouc-Wen oscillator by a third order linear system. Next, this third order linear system is reduced to a second order linear oscillator characterized by a set of ELPs by enforcing equality of certain response statistics of the two linear systems. The proposed formulation is utilized in conjunction with quasi-stationary stochastic processes compatible with elastic response spectra commonly used to represent the input seismic action in earthquake resistant design of structures. Then, the derived ELPs are used to estimate the peak response of Bouc-Wen hysteretic oscillators without numerical integration of the nonlinear equation of motion; this is done in the context of linear response spectrum-based dynamic analysis. Numerical results pertaining to the elastic response spectrum of the current European aseismic code provisions (EC8) are presented to demonstrate the usefulness of the proposed approach. These results are supported by pertinent Monte Carlo simulations involving an ensemble of non-stationary EC8 spectrum compatible accelerograms. The proposed approach can hopefully be an effective tool in the preliminary aseismic design stages of yielding structures and structural members commonly represented by the Bouc-Wen hysteretic model within either a force-based or a displacement-based context

Stochastic Density Functional Theory: Real- and Energy-Space Fragmentation for Noise Reduction

Stochastic density functional theory (sDFT) is becoming a valuable tool for studying ground state properties of extended materials. The computational complexity of describing the Kohn-Sham orbitals is replaced by introducing a set of random (stochastic) orbitals leading to linear and often sub-linear scaling of certain ground-state observable at the account of introducing a statistical error. Schemes to reduce the noise are essential, for example, for determining the structure using the forces obtained from sDFT. Recently we have introduced two embedding schemes to mitigate the statistical fluctuations in the electron density and resultant forces on the nuclei. Both techniques were based on fragmenting the system either in real-space or slicing the occupied space into energy windows, allowing for a significant reduction of the statistical fluctuations. For chemical accuracy further reduction of the noise is required, which could be achieved by increasing the number of stochastic orbitals. However, the convergence is relatively slow as the statistical error scales as $1/\sqrt{N_\chi}$ according to the central limit theorem, where $N_\chi$ is the number of random orbitals. In this paper we combined the aforementioned embedding schemes and introduced a new approach that builds on overlapped fragments and energy windows. The new approach significantly lowers the noise for ground state properties such as the electron density, total energy, and forces on the nuclei, as demonstrated for a G-center in bulk silicon.Comment: The following article has been submitted to the Journal of Chemical Physic

Learning deterministic probabilistic automata from a model checking perspective

Probabilistic automata models play an important role in the formal design and analysis of hard- and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification

Motion of inertial particles in Gaussian fields driven by an infinite-dimensional fractional Brownian motion

We study the motion of an inertial particle in a fractional Gaussian random field. The motion of the particle is described by Newton's second law, where the force is proportional to the difference between a background fluid velocity and the particle velocity. The fluid velocity satisfies a linear stochastic partial differential equation driven by an infinite-dimensional fractional Brownian motion with arbitrary Hurst parameter H in (0,1). The usefulness of such random velocity fields in simulations is that we can create random velocity fields with desired statistical properties, thus generating artificial images of realistic turbulent flows. This model captures also the clustering phenomenon of preferential concentration, observed in real world and numerical experiments, i.e. particles cluster in regions of low vorticity and high strain rate. We prove almost sure existence and uniqueness of particle paths and give sufficient conditions to rewrite this system as a random dynamical system with a global random pullback attractor. Finally, we visualize the random attractor through a numerical experiment.Comment: 30 pages, 1 figur