Machine learning for lattice field theory and beyond

Europe/Rome
Aula Renzo Leonardi (ECT*)

Aula Renzo Leonardi

ECT*

Strada delle Tabarelle 286, I-38123 Villazzano (Trento)
Biagio Lucini (Swansea University), Daniel Hackett (Massachusetts Institute of Technology, United States), Dimitrios Bachtis (ENS, Paris), Gert Aarts (Swansea University, UK), Phiala Shanahan (Massachusetts Institute of Technology, United States)
Description

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. 

     

Participants
Zoom Meeting ID
82630520227
Host
Michela Chiste'
Zoom URL