The scientific case for DeepRTP.

Real-time dynamics

The dynamics of strongly correlated quantum fields is at the heart of many pressing questions of modern theoretical physics. How does the Quark-Gluon-Plasma in a heavy-ion collision develop over time? How is energy and momentum exchanged among the microscopic constituents of ultracold quantum gases? Neither of these questions has been answered from first principles to date.

Deep Learning

Machine learning has developed at an astonishing pace over the past decade. In DeepRTP we apply deep learning to the exploration of real-time dynamics of quantum fields in two ways. On the one hand we develop novel methods to extract real-time dynamics from conventional Euclidean time simulations. On the other hand we attempt to use machine learning to implement direct simulations in Minkowski time.

Project Timeline

  • December 2018

    RCN approval

    The DeepRTP project successfully passes review by the Research Council of Norway and is funded as Young-Research-Talent grant in the FRIPRO category.

  • August 2019

    PhD student arrival

    We welcome our newest project member Gaurang Parkar, who will work on his PhD as part of the DeepRTP project.

  • November 2019

    Workshop at Yukawa Institute

    DeepRTP attended the Deep Learning and Physics (DLAP) workshop at the Yukawa Institute at the University of Kyoto.

  • Stay
    tuned for

Team Members

Alexander Rothkopf

Principal Investigator & Team Leader

Gaurang Parkar

PhD student

Jan M. Pawlowski

Collaborator at Heidelberg University

Tore S. Kleppe

Collaborator at UiS


The University of Stavanger,
Kjølv Egelands hus, Kristine Bonnevies vei 22,
4021 Stavanger, Norway