The past few years have seen rapid exploration of how machine learning (ML) techniques can be applied in theoretical particle and nuclear physics, particularly in numerical lattice quantum field theory (LQFT). Promising early works have already demonstrated potential for ML to accelerate computationally demanding LQFT calculations and add new capabilities to the LQFT toolkit.
This workshop aims to provide a forum for the community working on this topic to cross-pollinate methods, generate ideas for new applications, and assess the state of the field to guide further exploration. Highlighted topics include generative models for configuration generation, ML- accelerated algorithms, ML approaches to inverse problems, physics from novel machine-learned observables, and new calculational techniques enabled by ML methods.
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