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Jun-Liang Fu

Publications and source records attributed to Jun-Liang Fu.

2 recordsLinked to original sources

The Calderón problem for the multi-fractional Laplacian Schrödinger equation with drift terms

This paper studies the Calderón inverse problem for the multi-fractional Laplacian Schrödinger equation with drift terms, whose leading operator is a sum of fractional Laplacians of different orders, perturbed by a drift vector field and a potential. We consider two inverse settings: in the homogeneous source case \(f=0\), we aim to simultaneously recover the drift coefficient \(b\) and the potential \(q\) from the associated Dirichlet-to-Neumann map; in the non-homogeneous case \(f\neq 0\), we aim to simultaneously determine \(b\), \(q\), and the source term \(f\). Under suitable assumptions, we establish the well-posedness of the forward problem, derive a strong unique continuation principle, prove Runge-type approximation properties in fractional Sobolev spaces and weighted \(C^k\) spaces, and obtain generalized Alessandrini identities. These tools yield global uniqueness results for the inverse problems. In addition, we develop a numerical reconstruction framework combining finite difference discretization, radial basis function surrogate modeling, and hierarchical Bayesian inversion. Numerical examples in one dimension for smooth and discontinuous coefficients and sources illustrate the effectiveness of the proposed method and support the theoretical uniqueness results.

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An efficient method for reconstructing the potential in the fractional Calderón problem with a single measurement

In this paper, we develop a numerical method for determining the potential function in one-dimensional and two-dimensional fractional-order Calderón problems through a single measurement. The fractional Laplacian is discretized using the finite difference method, and the POD-RBF method is used to construct the forward problem surrogate model that can provide the predicted value and its exact gradient in time. The inverse problem is solved using Tikhonov regularization and the conjugate gradient method. The efficiency and effectiveness of the developed method are demonstrated through numerical experiments, and the theoretical results are verified.

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