9,160 research outputs found
Statistical solutions of hyperbolic conservation laws I: Foundations
We seek to define statistical solutions of hyperbolic systems of conservation
laws as time-parametrized probability measures on -integrable functions. To
do so, we prove the equivalence between probability measures on spaces
and infinite families of \textit{correlation measures}. Each member of this
family, termed a \textit{correlation marginal}, is a Young measure on a
finite-dimensional tensor product domain and provides information about
multi-point correlations of the underlying integrable functions. We also prove
that any probability measure on a space is uniquely determined by certain
moments (correlation functions) of the equivalent correlation measure.
We utilize this equivalence to define statistical solutions of
multi-dimensional conservation laws in terms of an infinite set of equations,
each evolving a moment of the correlation marginal. These evolution equations
can be interpreted as augmenting entropy measure-valued solutions, with
additional information about the evolution of all possible multi-point
correlation functions. Our concept of statistical solutions can accommodate
uncertain initial data as well as possibly non-atomic solutions even for atomic
initial data.
For multi-dimensional scalar conservation laws we impose additional entropy
conditions and prove that the resulting \textit{entropy statistical solutions}
exist, are unique and are stable with respect to the -Wasserstein metric on
probability measures on
Nash Equilibrium and Robust Stability in Dynamic Games: A Small-Gain Perspective
This paper develops a novel methodology to study robust stability properties
of Nash equilibrium points in dynamic games. Small-gain techniques in modern
mathematical control theory are used for the first time to derive conditions
guaranteeing uniqueness and global asymptotic stability of Nash equilibrium
point for economic models described by functional difference equations.
Specification to a Cournot oligopoly game is studied in detail to demonstrate
the power of the proposed methodology
A survey on fuzzy fractional differential and optimal control nonlocal evolution equations
We survey some representative results on fuzzy fractional differential
equations, controllability, approximate controllability, optimal control, and
optimal feedback control for several different kinds of fractional evolution
equations. Optimality and relaxation of multiple control problems, described by
nonlinear fractional differential equations with nonlocal control conditions in
Banach spaces, are considered.Comment: This is a preprint of a paper whose final and definite form is with
'Journal of Computational and Applied Mathematics', ISSN: 0377-0427.
Submitted 17-July-2017; Revised 18-Sept-2017; Accepted for publication
20-Sept-2017. arXiv admin note: text overlap with arXiv:1504.0515
A spectral-based numerical method for Kolmogorov equations in Hilbert spaces
We propose a numerical solution for the solution of the
Fokker-Planck-Kolmogorov (FPK) equations associated with stochastic partial
differential equations in Hilbert spaces.
The method is based on the spectral decomposition of the Ornstein-Uhlenbeck
semigroup associated to the Kolmogorov equation. This allows us to write the
solution of the Kolmogorov equation as a deterministic version of the
Wiener-Chaos Expansion. By using this expansion we reformulate the Kolmogorov
equation as a infinite system of ordinary differential equations, and by
truncation it we set a linear finite system of differential equations. The
solution of such system allow us to build an approximation to the solution of
the Kolmogorov equations. We test the numerical method with the Kolmogorov
equations associated with a stochastic diffusion equation, a Fisher-KPP
stochastic equation and a stochastic Burgers Eq. in dimension 1.Comment: 28 pages, 10 figure
Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this paper, the problem of stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic Cohen–Grossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.This work was supported by the Natural Science Foundation of CQ CSTC under grant 2007BB0430, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany
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