Beijing Institute of Mathematical Sciences and Applications

Yunfeng Cai

Songting Luo

Monday, June 15, 2026 10:30 AM - 11:30 AM

A3-4-101

Zoom 815 762 8413 (BIMSA)

Computational wave imaging is vital for uncovering hidden properties in diverse fields of science and engineering, such as seismic imaging and medical imaging. Machine learning has become a prominent method for these inverse problems. But its efficacy relies largely on labeled data, which in turn requires costly experiments and expertise requirements. Numerical simulation of the related physical models provides an important alternative for data acquisition. But simulating wave propagation is highly nontrivial, especially for high frequency cases and stiff models. In this talk we will report some recent developments for high fidelity wave simulations, where we conduct sophisticated dispersion relation analysis to select appropriate absorbing potentials to restrict the simulation on a bounded domain, and introduce split exponential integrators that combine exponential integration with high-order operator splitting for efficient wave propagation. Equipped with high-order spatial approximations, the proposed methods are shown to be accurate without sacrificing efficiency, allowing for low sapling density and larger CFL numbers. Both stability analysis and numerical experiments justify the high fidelity of the methods.

Songting Luo is a professor in the department of mathematics at Iowa State University. He obtained his PhD degree from University of California at Irvine in 2009. Before joining Iowa State University, he was a visiting assistant professor at Michigan State University. His research is mainly in the areas of mathematical modeling, numerical analysis, scientific computing and machine learning, for problems and applications related to waves, optics and quantum mechanics, with support from Simons Foundation and National Science Foundation.