Conference: Conference on Frontiers in Applied and Computational Mathematics (FACM 2023): New trends in computational wave propagation and imaging

Project: Research project

Project Details


This grant supports the participation of graduate students, postdoctoral fellows, and junior faculty in the conference ``Frontiers in Applied and Computational Mathematics'' (FACM) to be held May 26-27, 2023 at NJIT. This 18th conference in the FACM meeting series will be devoted to promising recent research in the field of computational wave propagation and imaging. Wave scattering and imaging problems are pervasive in a variety of engineering and industrial applications. These include, for instance, the design of optoelectronic devices such as lasers and solar panels, stealth technology, medical imaging, and nondestructive testing of materials (in which a prescribed incident electromagnetic wave is used to illuminate a sample and identify the location and shape of cracks or defects). Another important example comes from microchip design, which involves challenging wave propagation problems in multilayer materials that call for new numerical methods. Despite numerous successes, challenges remain in, e.g., high-frequency problems, wave scattering in complex media, design of fast algorithms, and the application of new techniques based on machine-learning, among others. The conference brings together a diverse group of mathematicians, statisticians, scientists, and engineers to present their research in an environment that promotes significant interaction and cross-fertilization among the participants. The FACM 2023 conference theme is ``New trends in computational wave propagation and imaging,’’ and will focus on several problem areas that have either been established as areas of major significance in applied mathematics, or as emerging research fields with exceptional potential. These are: (1) inverse problems and imaging, (2) integral equation and high-frequency methods, (3) optimal transport in optical design, and (4) applications of machine learning in PDE's and inverse problems. The field of computational wave propagation and imaging has grown rapidly in the past two decades, fueled by industrial applications and supported by rapid development in mathematical theory and novel approaches stemming from advances in, for example, nonlinear optimization, randomized and fast linear solvers, and machine learning/data science. Despite the progress, numerous challenges remain, for instance, in treating high frequency problems with multiple scattering effects, computational inversion with limited (sparse, incomplete, or both) data, and high dimensional problems, among many others. In addition, there is a strong need for fundamental theory that can lead to a better understanding of the mathematical underpinnings of the new algorithms. FACM 2023 presents an ideal forum to stimulate new strategies to tackle these challenging problems through cross-fertilization of ideas and new collaborations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Effective start/end date5/15/234/30/24


  • National Science Foundation: $34,778.00


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