Necessary conditions for the multiple integral problem and elliptic variational inequalities
The parabolic equations driven by linearly multiplicative Gaussian noise are stabilizable in probability by linear feedback controllers with support in a suitably chosen open subset of the domain. This procedure extends to Navier − Stokes equations with multiplicative noise. The exact controllability is also discussed.
The liner parabolic equation ∂y ∂t − 1 2 Δy + F · ∇ y = 1 x1d4aa; 0 u with Neumann boundary condition on a convex open domain x1d4aa; ⊂ ℝ with smooth boundary is exactly null controllable on each finite interval if 𝒪is an open subset of x1d4aa; which contains a suitable neighbourhood of the recession cone of x1d4aa; . Here, : ℝ → ℝ is a bounded, -continuous function, and = ∇, where is convex and coercive.
One shows that the linearized Navier-Stokes equation in , around an unstable equilibrium solution is exponentially stabilizable in probability by an internal noise controller , , where are independent Brownian motions in a probability space and is a system of functions on with support in an arbitrary open subset . The stochastic control input is found in feedback form. One constructs also a tangential boundary noise controller which exponentially stabilizes in probability the equilibrium...
One proves that the steady-state solutions to Navier–Stokes equations with internal controllers are locally exponentially stabilizable by linear feedback controllers provided by a control problem associated with the linearized equation.
We prove the analyticity of -concave sets of locally finite Hausdorff -measure in a -dimensional complex space. We apply it to give a removability criterion for meromorphic maps with values in -complete spaces.
One shows that the linearized Navier-Stokes equation in , around an unstable equilibrium solution is exponentially stabilizable in probability by an internal noise controller , , where are independent Brownian motions in a probability space and is a system of functions on with support in an arbitrary open subset . The stochastic control input is found in feedback form. One constructs also a tangential boundary noise controller which exponentially stabilizes in probability the equilibrium solution. ...
We consider a convexity notion for complex spaces with respect to a holomorphic line bundle over . This definition has been introduced by Grauert and, when is analytically trivial, we recover the standard holomorphic convexity. In this circle of ideas, we prove the counterpart of the classical Remmert’s reduction result for holomorphically convex spaces. In the same vein, we show that if separates each point of , then can be realized as a Riemann domain over the complex projective space...
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