Authors: Sascha Diefenbacher, Sofia Palacios Schweitzer, Gregor Kasieczka
Organizations: Institut für Theoretische Physik, Universität Heidelberg, Germany · Physics Division, Lawrence Berkeley National Laboratory, Berkeley, USA · NHETC, Department of Physics & Astronomy, Rutgers University, Piscataway, NJ, USA · Institut für Experimentalphysik, Universität Hamburg, Germany
Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.