Search arXivSearch

arXiv · 1905.05005

Fefferman's Inequality and Applications in Elliptic Partial Differential Equations

Abstract

In this paper we prove Fefferman's inequalities associated to potentials belonging to a generalized Morrey space $ L^{p,φ} $ or a Stummel class $ \tilde{S}_{α,p} $. Our results generalize and extend Fefferman's inequalities obtained in \cite{CRR,CF,F,Z1}. We also show that the logarithmic of non-negative weak solution of second order elliptic partial differential equation, where its potentials are assumed in generalized Morrey spaces and Stummel classes, belongs to the bounded mean oscillation class. As a consequence, this elliptic partial differential equation has the strong unique continuation property. An example of an elliptic partial differential equation where its potential belongs to certain Morrey spaces or Stummel classes which does not satisfy the strong unique continuation is presented.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nicky K. Tumalun, Denny I. Hakim, Hendra Gunawan. 2020-03-15. Fefferman's Inequality and Applications in Elliptic Partial Differential Equations. https://arxiv.org/abs/1905.05005

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Two-layers neural networks for Schr{ö}dinger eigenvalue problems

The aim of this article is to analyze numerical schemes using two-layer neural networks with infinite width for the resolution of high-dimensional Schr{ö}dinger eigenvalue problems with smooth interaction potentials and Neumann boundary condition on the unit cube in any dimension. More precisely, any eigenfunction associated to the lowest eigenvalue of the Schr{ö}dinger operator is a unit L 2 norm minimizer of the associated energy. Using Barron's representation of the solution with a probability measure defined on the set of parameter values and following the approach initially suggested by Bach and Chizat [1], the energy is minimized thanks to a constrained gradient curve dynamic on the 2-Wasserstein space of the set of parameter values defining the neural network. We prove the existence of solutions to this constrained gradient curve. Furthermore, we prove that, if it converges, the represented function is then an eigenfunction of the considered Schr{ö}dinger operator. At least up to our knowledge, this is the first work where this type of analysis is carried out to deal with the minimization of non-convex functionals.

math.AP

Validity of Prandtl Expansion for Steady Compressible Navier-Stokes-Fourier Flows

Assume no-slip boundary conditions for the velocity field and either insulated or Dirichlet boundary conditions for the temperature field in a steady compressible fluid. In the inviscid limit $\v \rightarrow 0$, we develop a mathematical framework for the uniform-in-$\v$ remainder estimate for the linear steady compressible Navier-Stokes-Fourier equations around a Prandtl layer profile with both velocity and thermal layers, which leads to the validity of the Prandtl layer expansion.

math.AP

Long time behaviour of Mean Field Games with fractional diffusion

In this paper we study the long time behaviour of mean field games systems with fractional diffusion, modeling the case that the individual dynamics of the players is driven by independent jump processes and controlled through the drift term, while being confined by an external field in order to guarantee ergodicity. In the case of globally Lipschitz, locally uniformly convex Hamiltonian, and weakly coupled costs satisfying the Lasry-Lions monotonicity condition, we prove that there is a unique solution $(u_T,m_T)$ to the mean field game problem in $(0,T)$ and we show that, if $T$ is sufficiently large, $(u_T,m_T)$ satisfies the so-called turnpike property, namely it is exponentially close to the (unique) stationary ergodic state for any proportionally long intermediate time.

math.AP