Symmetries and Generalization for Machine Learning on a Lattice

Symmetries such as translations and rotations are crucial in physics and machine learning. The global symmetry of translations leads to convolutional neural networks (CNNs), while the much larger space of local gauge symmetry has driven us to develop lattice gauge equivariant convolutional neural networks (L-CNNs). This talk will discuss how the challenges of simulating the earliest stage of heavy ion collisions led us to use machine learning and how these innovations could improve lattice simulations in the future.

Andreas Ipp is a Senior Scientist at the Institute for Theoretical Physics at TU Wien. He received his PhD in 2003 and held postdoctoral positions at ECT* in Trento and the Max-Planck-Institute in Heidelberg before returning to TU Wien in 2009. He completed his habilitation on "Yoctosecond dynamics of the quark-gluon plasma" in 2014. His current research focuses on symmetries in machine learning for applications in lattice gauge theory and heavy ion collisions.