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Measure differential inclusions

2018 IEEE Conference on Decision and Control (CDC), 2018
When modeling dynamical systems with uncertainty, one usually resorts to stochastic calculus and, specifically, Brownian motion. Recently, we proposed an alternative approach based on time-evolution of measures, called Measure Differential Equations, which can be seen as natural generalization of Ordinary Differential Equations to measures.
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One-sided Perron Differential Inclusions

Set-Valued and Variational Analysis, 2013
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Donchev, Tzanko   +2 more
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ε-approximation of differential inclusions

Proceedings of 1995 34th IEEE Conference on Decision and Control, 1996
For a Lipschitz differential inclusion x ∈ f(x), we give a method to compute an arbitrarily close approimation of Reachf(X0, t) — the set of states reached after time t starting from an initial set X0. For a differential inclusion x ∈ f(x), and any e>0, we define a finite sample graph A∈. Every trajectory φ of the differential inclusion x ∈f(x) is also
Anuj Puri, Vivek Borkar, Pravin Varaiya
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Fuzzy differential inclusions

Journal of Applied Mathematics and Mechanics, 1990
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Stochastic Invariance for Differential Inclusions

Set-Valued Analysis, 2000
The first objective of this paper is to combine two ways for representing uncertainty through stochastic differential inclusions: a stochastic uncertainty driven by a Wiener process and a contingent uncertainty driven by a set-valued map. The second point consists to extend to stochastic differential inclusions the invariance theorem for nonstochastic ...
Aubin, Jean-Pierre   +2 more
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Impulsive Differential Inclusions

2013
Impulsive differential equations have been developed in modeling impulsive problems in physics, population dynamics, ecology, biotechnology, industrial robotics, pharmacokinetics, optimal control, etc. The questions of existence and stability of solutions for different classes of initial values problems for impulsive differential equations and ...
John R. Graef   +2 more
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Partial Differential Inclusions

2013
The present chapter is devoted to partial differential inclusions described by the semielliptic set-valued partial differential operators \(\mathbb{L}_{FG}\) generated by given set-valued mappings F and G. Such inclusions will be investigated by stochastic methods. As in the theory of ordinary differential inclusions, the existence of solutions of such
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Differential Inclusions

2008
Jean-Pierre Aubin, Hèléne Frankowska
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Differential Inclusions

1984
Jean-Pierre Aubin, Arrigo Cellina
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