Machine Learning for High Energy Physics, on and off the Lattice
Machine learning (ML) has been recently used as a very effective tool for the study and prediction of data in various fields of physics, from statistical physics to theoretical high energy physics. The aim of this workshop is to bring together active researchers on ML and Physics to interact and initiate a collaborative effort to investigate timely problems on Lattices and Theoretical High Energy Physics. Hence we invite scientists with research areas covering a broad spectrum to present their work. Some of the topics which will be highlighted are supervised and unsupervised identification of phase transitions on lattice models, applications of generative algorithms in the production of lattice configurations, applications of machine learning estimators in observables in Lattice QCD and the connection of ML with Renormalization Group as well as the gauge/gravity correspondence.
The workshop will take place as a hybrid meeting, with limited on-site participation, provided that the pandemic situation permits this.
Andreas Athenodorou (Università di Pisa)
Dimitrios Giataganas (National Sun Yat-sen University)
Biagio Lucini (Swansea University)
Enrico Rinaldi (University of Michigan)
Kyle Cranmer (New York University)
Costantia Alexandrou (University of Cyprus & The Cyprus Institute)
Registration no longer available.
Registration is available from 27/07/2021 until 23/09/2021.
- Registration deadline for in-person participation: August 27, 2021
- Registration deadline for remote participation: September 23, 2021