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Explore data-assisted algorithms for inverse random source scattering problems in this 53-minute webinar presented by Ying Liang for the Data-Driven Physical Simulations (DDPS) series. Learn about a novel approach that utilizes boundary measurement data to reconstruct statistical properties of random sources with fewer realizations than traditional methods. Discover how this technique achieves better reconstruction using only 1/10 of the realizations required by conventional approaches. Compare the performance of various data-driven algorithms, with a focus on Image-to-Image translation methods like pix2pix for reconstructing well-separated inclusions. Gain insights into the stability of this approach with respect to observation data noise and its applications in fields such as antenna synthesis, medical imaging, and earthquake monitoring.
