scispace - formally typeset
I

Igor G. Vladimirov

Researcher at Australian National University

Publications -  129
Citations -  1075

Igor G. Vladimirov is an academic researcher from Australian National University. The author has contributed to research in topics: Stochastic differential equation & Gaussian. The author has an hindex of 13, co-authored 117 publications receiving 927 citations. Previous affiliations of Igor G. Vladimirov include State Scientific Research Institute of Aviation Systems & Queen Mary University of London.

Papers
More filters
Journal ArticleDOI

Tracing Diffusion in Porous Media with Fractal Properties

TL;DR: This work employs a diffusion tracer as a reference scalar field that makes the conditional averaging sensitive to the proximity of a point to the interface, and considers a hitting time stochastic interpretation of the tracer.
Posted Content

Lie-algebraic connections between two classes of risk-sensitive performance criteria for linear quantum stochastic systems.

TL;DR: A Lie algebraic connection is discussed between these two classes of cost functionals for open quantum harmonic oscillators using an apparatus of complex Hamiltonian kernels and symplectic factorizations to extend useful properties from one of the classes of risk-sensitive costs to the other.
Journal ArticleDOI

Frequency measurability, algebras of quasiperiodic sets and spatial discretizations of smooth dynamical systems

TL;DR: In this paper, a probability theoretical approach is developed which is based on equipping the grid with an algebra of frequency-measurable quasiperiodic subsets characterized by frequency functions.
Posted Content

Anisotropic Norm Bounded Real Lemma for Linear Discrete Time Varying Systems

TL;DR: In this article, the authors consider a finite horizon linear discrete time varying system whose input is a random noise with an imprecisely known probability law, and the statistical uncertainty is described by a nonnegative parameter a which constrains the anisotropy of the noise as an entropy theoretic measure of deviation of the actual noise distribution from Gaussian white noise laws with scalar covariance matrices.
Posted Content

Anisotropy-based optimal filtering in linear discrete time invariant systems.

TL;DR: An anisotropic estimator is constructed which minimizes the worst-case error-to-noise RMS ratio of the root-mean-square value of the estimation error of the disturbance over stationary Gaussian disturbances whose mean anisotropy is bounded from above by a given parameter.