DDPS | Data-assisted Algorithms for Inverse Random Source Scattering Problems by Ying Liang скачать в хорошем качестве

DDPS | Data-assisted Algorithms for Inverse Random Source Scattering Problems by Ying Liang

Inverse source scattering problems are essential in various fields, including antenna synthesis, medical imaging, and earthquake monitoring. In many applications, it is necessary to consider uncertainties in the model, and such problems are known as stochastic inverse problems. Traditional methods require a large number of realizations and information on medium coefficients to achieve accurate reconstruction for inverse random source problems. To address this issue, we propose a data-assisted approach that uses boundary measurement data to reconstruct the statistical properties of the random source with fewer realizations. We compare the performance of different data-driven algorithms under this framework to enhance the initial approximation obtained from integral equations. Our numerical experiments demonstrate that the data-assisted approach achieves better reconstruction with only 1/10 of the realizations required by traditional methods. Among the various Image-to-Image translation algorithms that we tested, the pix2pix method outperforms others in reconstructing well-separated inclusions with accurate positions. Our proposed approach results in stable reconstruction with respect to the observation data noise. Bio: Ying Liang is a Golomb Visiting Assistant Professor of Mathematics at the Department of Mathematics of Purdue University. She earned her PhD degree in Mathematics from The Chinese University of Hong Kong in 2021, under the supervision of Prof. Jun Zou. Her broad research area is in computational and applied mathematics. Her current research interests include ill-posed inverse problems, Machine learning, numerical methods for partial differential equations, and scattering theory. DDPS webinar: https://www.librom.net/ddps.html 💻 LLNL News: https://www.llnl.gov/news 📲 Instagram:   / livermore_lab   🤳 Facebook:   / livermore.lab   🐤 Twitter:   / livermore_lab   🔔 Subscribe: / livermorelab About LLNL: Lawrence Livermore National Laboratory has a mission of strengthening the United States’ security through development and application of world-class science and technology to: 1) enhance the nation’s defense, 2) reduce the global threat from terrorism and weapons of mass destruction, and 3) respond with vision, quality, integrity and technical excellence to scientific issues of national importance. Learn more about LLNL: https://www.llnl.gov/. LLNL-VIDEO-849168 #LLNL LivermoreLab #DataDrivenPhysicalSimulations