Abstract: Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems. In this paper, we reveal that recent methods ...

Join us, October 26, 27 and 28, 2022, for our new lecture series named after Vladimir Marchenko, a Ukrainian mathematician who specializes in mathematical physics. Marchenko's seminal contributions to ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Abstract: Many electromagnetic design problems can be cast as an inverse problem. That is, one may specify a desired scattering state and seek to find the ideal configuration of an antenna, waveguide, ...

Bayesian statistics remain popular for addressing inverse problems, whereby quantities of interest are determined from their noisy and indirect observations. Bayes’ theorem forms the foundation of ...

Abstract: In recent years, there has been notable progress in the development of inverse problems for image reconstruction in pulse-echo ultrasound. Inverse problems are designed to circumvent the ...

Latent diffusion models have been demonstrated to generate high-quality images, while offering efficiency in model training compared to diffusion models operating in the pixel space. However, ...

University of California, Santa Cruz professor of mathematics François Monard has received the prestigious Calderón Prize for his work in the field of inverse problems. Broadly defined, researchers ...

Geophysical inverse problems face a lot of issues to be solved, e.g., data noise problems: the general data records contain a lot of noise due to various factors in the excitation, propagation and ...

Shifting focus from family-friendly entertainment to politics and ideological wars, Disney had a rough 2022 as its two biggest "woke" flicks flopped at the box office. From legal battles with Fla. Gov ...