Research Cluster: Wave Propagation and Imaging in Random Media (September 6 - October 13, 2017)
Wave propagation and imaging in complex environments is an important topic in applied mathematics with a wide range of applications, including not only radar and seismic reconstruction but also many others, such as laser beam propagation through clouds, light propagation through the atmosphere in astronomy, secure communications in scattering media, medical imaging, and nondestructive evaluation of materials. This cluster will involve contemporary topics on waves in random media. Recent progress in this area has been motivated on the one hand by a range of applications that involve partly or fully incoherent waves, such as time reversal, active-array imaging, passive imaging and hybrid imaging, and on the other hand by advances in sensor technology that have brought new and massive amounts of data.
Mathematically, the problem of waves in random media may be placed in a stochastic framework, where the complex environments are realizations of random fields and the scattering regimes are determined by important physical scales, such as the wavelength, the distance of propagation, and the scale of the fluctuations. The theory for waves in random media is concerned in particular with describing the statistics of the wave field in a concise manner. It is fundamental to applications in imaging through complex environments.
This cluster brings together scientists with expertise in stochastic analysis and theoretical, numerical, and experimental wave propagation and imaging, random matrix theory, and compressed sensing with the goal of exchanging ideas and advancing the field. The main topics of interest are:
- Theoretical and numerical studies of wave propagation in random media in a variety of scattering regimes and setups.
- Inverse source problems and inverse scattering problems in random media.
- Imaging with distributed and opportunistic noise sources.
- Applications of random matrix theory for improved detection and imaging in strong random media.
- Imaging of sparse scenes and connections to compressed sensing.
- Time dependent problems, where the medium or the scatterers to be imaged are in unknown motion.
- Alexandre Aubry
(ESPCI Paris Tech)
- Liliana Borcea
(University of Michigan)
- Albert Fannjiang
(University of California at Davis)
- Knut Solna
(University of California at Irvine)
- Chrysoula Tsogka
(University of Crete)
(September 25-29, 2017)