Speaker
Austin Hoover
(Oak Ridge National Laboratory)
Description
Generative models can be trained to reproduce low-dimensional projections of high-dimensional phase space distributions. Normalizing flows are generative models that parameterize invertible transformations, allowing exact probability density evaluation and sampling. Consequently, flows are unbiased entropy estimators and could be used to solve the high-dimensional maximum-entropy tomography (MENT) problem. In this work, we evaluate a flow-based MENT solver (MENT-Flow) against exact maximum-entropy solutions and Minerbo's iterative MENT algorithm in two dimensions.
| Region represented | North America |
|---|---|
| Paper preparation format | LaTeX |
Primary author
Austin Hoover
(Oak Ridge National Laboratory)
Co-author
Chun Yan Jonathan Wong
(Institute of Modern Physics, Chinese Academy of Sciences)
