Speaker
Gurtej Kanwar
(MIT)
Description
Critical slowing down and topological freezing severely hinder Monte Carlo sampling of lattice field theories as the continuum limit is approached. Recently, significant progress has been made in applying a class of generative machine learning models, known as "flow-based" samplers, to combat these issues. These generative samplers also enable promising practical improvements in Monte Carlo sampling, such as fully parallelized configuration generation. In this talk, I will discuss the progress towards this goal and future prospects of the method.
| Topical area | Algorithms and Artificial Intelligence |
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Primary author
Gurtej Kanwar
(MIT)