TBA

• Mike Douglas

Room: 02.430

TBA

• Ed HIrst

Room: 02.430

TBA

• Fabian Ruehle

Room: 02.430

TBA

• Vasilis Niarchos

Room: 02.430

TBA

• Gary Shiu

Room: 02.430

TBA

• Mark Hughes

Room: 02.430

Quantum field theory and deep neural networks

• Ro Jefferson

Room: 02.430

Short talks /round of introductions

• Maiti
• Abel
• Gerdes

Room: 02.430

Machine learning the bulk in AdS/CFT

• Koji Hashimoto

Room: 02.430

Short talks

• Mathis Gerdes
• Steven Abel
• Anindita Maiti

Room: 02.430

TBA

• Phiala Shanahan

Room: 02.430

Learning QFT in examples: DOING reconstructions, LEARNING new obserbales, and EMULATING simulations

• Jan Pawlowski

Room: 02.430

Discussion Session Room: 02.430

Generative models, manifolds and symmetries: From QFT to molecules

• Danilo Rezende

Room: 02.430 The study of symmetries in physics has revolutionized our understanding of the world. Inspired by this, the development of methods to incorporate internal (Gauge) and external (space-time) symmetries into machine learning models is a very active field of research. We will introduce some of the latest work in the field. We will then present our latest work on invariant generative models and its applications to lattice-QCD and molecular dynamics simulations. In the molecular dynamics front, we'll talk about how we constructed permutation and translation-invariant normalizing flows on a torus for free-energy estimation. In lattice-QCD, we'll present our work that introduced the first U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions.