Seminars

Date: March 13, 10:00–11:00

Venue: Zoom Meeting

Speaker: Prof. Won-Kwang Park (Kookmin University)

Title: Real-Time identification of small dielectric objects from two-dimensional Fresnel experimental dataset

Abstract: We consider an inverse scattering problem for the real-time localization of unknown scatterers from measured scattered-field data. In most studies, all elements of the so-called multi-static response (MSR) matrix, whose entries correspond to the measured scattered fields, must be collected to design an appropriate indicator function. However, in various experimental setups, such as the Fresnel experimental facility or microwave imaging systems at ETRI, some elements cannot be measured. Motivated by this limitation, we consider the inversion of Fresnel experimental dataset to retrieve the existence, location, outline shape of small dielectric scatterers. In order to show the applicability and limitation, we demonstrate that the imaging function can be expressed in terms of the material properties of the objects, an infinite series of integer-order Bessel functions, and the configuration of the transmitting and receiving antennas. We further investigate the quantitative relationship between imaging performance and the sparsity of the MSR matrix. Numerical simulations using the 2D Fresnel dataset are conducted to validate our theoretical results.

Date: May 22, 10:00–11:00

Venue: Zoom Meeting

Speaker: Prof. Sunghwan Moon, (Kyungpook National University)

Title: A Unified Physics-Informed Self-Supervised Framework for PDE Inverse Problems: Applications to Photoacoustic and Elliptic Imaging

Abstract: In this talk, we present a unified learning framework for inverse problems governed by wave and elliptic partial differential equations (PDEs), where the forward operator is unknown and no ground-truth interior data is available.

The key idea is to embed a physics-based forward solver directly into the training loop, enabling learning from boundary measurement data alone. This removes the need for supervised training pairs and allows simultaneous recovery of unknown quantities.

The framework is applied to three representative problems:

(1) a nonlinear photoacoustic model where the sound speed depends on the unknown initial pressure,

(2) a wave inverse problem with spatially varying unknown sound speed, connected to Calderón-type structures,

(3) an elliptic inverse problem based on the Dirichlet-to-Neumann map, where theoretical uniqueness is available.

Numerical results demonstrate robustness under noise. This work suggests a general paradigm for solving PDE inverse problems via physics-informed self-supervised learning.