Puchert, Patrik http://orcid.org/0000-0002-0654-0433
Hermosilla, Pedro
Ritschel, Tobias
Ropinski, Timo
Funding for this research was provided by:
Bundesministerium für Wirtschaft und Energie (ZF4483101ED7)
Universität Ulm
Article History
Received: 4 December 2020
Accepted: 26 June 2021
First Online: 21 July 2021
Declarations
:
: The authors declare that they have no conflict of interest.
: The code for the proposed DDE method, including density estimation, model training and synthetic data generation along with the trained models is available onas well as in the python package deep_density_estimation.