Applied Mathematics, Inverse Problem, Machine Learning, Data Science
Apply complex data to address interdisciplinary challenges spanning science, engineering, and technology
Kernel machine learning for inverse source and scattering problems, preprint (with B. Zhang)
Shape and parameter identification by the linear sampling method for a restricted Fourier integral operator, preprint (with L. Audibert)
Data-driven Basis for Reconstructing the Contrast in Born Inverse Scattering: Picard Criterion, Regularity, Regularization and Stability, arXiv preprint arXiv:2211.10192, accepted SIAM J. Appl. Math.
Single Mode Multi-frequency Factorization Method for the Inverse Source Problem in Acoustic Waveguides, SIAM J. Appl. Math. 83. (2), 394-417 (2023)
Data completion algorithms and their applications in inverse acoustic scattering with limited-aperture backscattering data, Journal of Computational Mathematics 469, 111550 (2022) (with F.Dou, X. Liu, and B. Zhang)
Asymptotic anatomy of the Berry phase for scalar waves in 2D periodic continua, Proceedings of the Royal Society A 478, 2262, (2022) (with B. Guzina and O. Oudghiri-Idrissi)
Modified sampling method with near field measurements, SIAM J. Appl. Math. 82 (1), 244-266 (2022) (with X. Liu and B. Zhang)
“Recent Advances in Inverse Scattering: Theory and Applications” as part of the conference “Inverse Problems: Modeling and Simulation” (IPMS 2024) Malta, in May 2024.
Dr. Meng gave two invited talks (minisymposium) at Applied Inverse Problems 2023, Göttingen, Germany, September 2023.
Dr. Meng gave an invited talk at the workshop NMSP2023, Hokkaido, Japan, August, 2023.
Dr. Meng gave an invited talk (minisymposium) at ICIAM2023, Tokyo, Japan, August, 2023.