Decay rates for solutions of a Timoshenko system with a memory condition at the boundary.
This note is concerned with the linear Volterra equation of hyperbolic type on the whole space ℝN. New results concerning the decay of the associated energy as time goes to infinity were established.
The existence of decaying positive solutions in of the equations and displayed below is considered. From the existence of such solutions for the subhomogeneous cases (i.e. as ), a super-sub-solutions method (see § 2.2) enables us to obtain existence theorems for more general cases.
Using the idea of the optimal decomposition developed in recent papers (Edmunds-Krbec, 2000) and in Cruz-Uribe-Krbec we study the boundedness of the operator Tg(x) = ∫x1 g(u)du / u, x ∈ (0,1), and its logarithmic variant between Lorentz spaces and exponential Orlicz and Lorentz-Orlicz spaces. These operators are naturally linked with Moser's lemma, O'Neil's convolution inequality, and estimates for functions with prescribed rearrangement. We give sufficient conditions for and very simple proofs...
Soit une variété analytique complexe et son fibre cotangent. Soit un module cohérent sur l’anneau des opérateurs microdifférentiels formels sur . Dans le cas ou le support (ou variété caractéristique) de est une hypersurface, B. Malgrange a démontre que se décompose en systèmes élémentaires au point générique et après tensorisation par l’anneau des opérateurs microdifférentiels d’ordre - fractionnaire avec approprie.Dans ce travail, on généralise le résultat cité : d’abord pour un...
We propose a deep learning method for the numerical solution of partial differential equations that arise as gradient flows. The method relies on the Brezis–Ekeland principle, which naturally defines an objective function to be minimized, and so is ideally suited for a machine learning approach using deep neural networks. We describe our approach in a general framework and illustrate the method with the help of an example implementation for the heat equation in space dimensions two to seven.