57 citations · 165 across the 9 of their papers we have counts for
10 papers
HMC and gradient flow with machine-learned classically perfect fixed-point actions
Kieran Holland, Andreas Ipp, David I. Müller +1
Fixed-point (FP) lattice actions are classically perfect, i.e., they have continuum classical properties unaffected by discretization effects and are expected to have suppressed la…
A stabilizing kernel for complex Langevin simulations of real-time gauge theories
Kirill Boguslavski, Paul Hotzy, David I. Müller
The complex Langevin (CL) method is a promising approach to overcome the sign problem, which emerges in real-time formulations of quantum field theories. Over the past decade, stab…
Equivariance and generalization in neural networks
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
The crucial role played by the underlying symmetries of high energy physics and lattice field theories calls for the implementation of such symmetries in the neural network archite…
Generalization capabilities of neural networks in lattice applications
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories. An essential element of such theories is represented by symme…
Transverse momentum broadening in real-time lattice simulations of the glasma
Andreas Ipp, David I. Müller, Daniel Schuh
The study of jets in heavy-ion collisions provides important information about the interaction of partons with the medium that they traverse. The seeds of jets are highly energetic…
Jet momentum broadening in the pre-equilibrium Glasma
Andreas Ipp, David I. Müller, Daniel Schuh
Jets are important probes of heavy ion collisions as they can provide information on the interaction of a highly energetic parton with the medium it traverses. In the hydrodynamic…