| Wednesday July 24 | ||
| 09:45-10:00 | Gert Aarts | Opening |
| 10:00-10:40 | Lingxiao Wang | Learning hadron interactions from lattice QCD |
| 10:50-11:30 | Simran Singh | Testing machine learning against finite size scaling for the chiral phase transition |
| 12:30 | Lunch | |
| 14:00-14:40 | Elia Cellini | Stochastic normalizing flows for new theories and observables |
| 14:50-15:30 | Alessandro Nada | Sampling SU(3) pure gauge theory with out-of-equilibrium evolutions and stochastic normalizing flows |
| Coffee break | ||
| 15:40-16:20 | Ankur Singha | Multilevel sampling of lattice theories using RG-inspired autoregressive models |
| Thursday July 25 | ||
| 09:30-10:10 | Tej Kanwar | Neural-network contour deformations for the signal-to-noise problem |
| 10:20-11:00 | Alexander Rothkopf | Learning optimal kernels for real-time complex Langevin |
| Coffee break | ||
| 11:30-12:10 | Biagio Lucini | Topological data analysis for lattice gauge theories |
| 12:30 | Lunch | |
| 14:00-14:40 | Ryan Abbott | Progress in normalizing flows for 4d gauge theories |
| 14:50-15:30 | Fernando Romero Lopez | Applications of flow models to the generation of correlated lattice QCD ensembles |
| Coffee break | ||
| 16:00-16:40 | Mathis Gerdes | Exploring continuous normalizing flows for gauge theories |
| Friday July 26 | ||
| 09:30-10:10 | Akio Tomiya | MLPhys in Japan and developments of CASK: Gauge symmetric transformer |
| 10:20-11:00 | David Müller | Lattice simulations with machine-learned classically perfect fixed-point actions |
| Coffee break | ||
| 11:30-12:10 | Chanju Park | Empirical phase diagram of neural networks and spin glass theory |
| 12:30 | Lunch | |
| 14:00-14:40 | Tomasz Stebel | Entanglement entropy with generative neural networks |
| 14:50-15:10 | Shiyang Chen | Exploring generative networks for manifolds with non-trivial topology |
| Coffee break | ||
| 15:40-16:20 | Gert Aarts | Weight matrix dynamics and Dyson Brownian motion |
| 16:30-16:50 | Matteo Favoni | Towards the application of random matrix theory to neural networks |