Principal Component Density Estimation for Scenario Generation Using Normalizing Flows. - Eike Cramer; Alexander Mitsos; Raúl Tempone; Manuel Dahmen (CoRR, 2021)

Validation Methods for Energy Time Series Scenarios From Deep Generative Models. - Eike Cramer; Leonardo Rydin Gorjão; Alexander Mitsos; Benjamin Schäfer 001; Dirk Witthaut; Manuel Dahmen (IEEE Access, 2022)

Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators. - Eike Cramer; Leonard Paeleke; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2022)

Nonlinear Isometric Manifold Learning for Injective Normalizing Flows. - Eike Cramer; Felix Rauh; Alexander Mitsos; Raúl Tempone; Manuel Dahmen (CoRR, 2022)

Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows. - Eike Cramer; Dirk Witthaut; Alexander Mitsos; Manuel Dahmen (CoRR, 2022)

Physics-inspired machine learning for power grid frequency modelling. - Johannes Kruse 001; Eike Cramer; Benjamin Schäfer 001; Dirk Witthaut (CoRR, 2022)

Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows. - Hannes Hilger; Dirk Witthaut; Manuel Dahmen; Leonardo Rydin Gorjão; Julius Trebbien; Eike Cramer (CoRR, 2023)

A black-box adversarial attack on demand side management. - Eike Cramer; Ji Gao (Comput. Chem. Eng., 2024)

Model-Free Stochastic Process Modeling and Optimization using Normalizing Flows. - Eike Cramer (CoRR, 2024)