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Unique continuation for the gradient of eigenfunctions and Wegner estimates for random divergence-type operators
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abstract
We prove a scale-free quantitative unique continuation estimate for the gradient of eigenfunctions of divergence-type operators, i.e. operators of the form $-\mathrm{div}A\nabla$, where the matrix function $A$ is uniformly elliptic. The proof uses a unique continuation principle for elliptic second order operators and a lower bound on the $L^2$-norm of the gradient of eigenfunctions corresponding to strictly positive eigenvalues. As an application, we prove an eigenvalue lifting estimate that allows us to prove a Wegner estimate for random divergence-type operators. Here our approach allows us to get rid of a restrictive covering condition that was essential in previous proofs of Wegner estimates for such models.
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Sampling and equidistribution theorems for elliptic second order operators, lifting of eigenvalues, and applications
Tautenhahn and Veselic correct an error in their 2020 proof and establish scale-free sampling and equidistribution estimates for eigenfunctions of elliptic second order operators with Lipschitz coefficients.
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