Integrated design of processes and products: Optimal renewable fuels. - Andrea König; Lisa Neidhardt; Jörn Viell; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2020)

COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization. - Marco Langiu; David Yang Shu; Florian Joseph Baader; Dominik Hering; Uwe Bau; André Xhonneux; Dirk Müller 005; André Bardow; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2021)

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

Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators. - Yue Guo 006; Felix Dietrich; Tom Bertalan; Danimir T. Doncevic; Manuel Dahmen; Ioannis G. Kevrekidis; Qianxiao Li (CoRR, 2021)

Incorporating AC Power Flow into the Multi-Energy System Optimization Framework COMANDO. - Philipp Glücker; Marco Langiu; Thiemo Pesch; Manuel Dahmen; Andrea Benigni (OSMSES, 2022)

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)

Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points. - Marco Langiu; Manuel Dahmen; Alexander Mitsos (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)

Graph Machine Learning for Design of High-Octane Fuels. - Jan G. Rittig; Martin Ritzert; Artur M. Schweidtmann; Stefanie Winkler; Jana M. Weber; Philipp Morsch; K. Alexander Heufer; Martin Grohe; Alexander Mitsos; Manuel Dahmen (CoRR, 2022)

Graph neural networks for the prediction of molecular structure-property relationships. - Jan G. Rittig; Qinghe Gao; Manuel Dahmen; Alexander Mitsos; Artur M. Schweidtmann (CoRR, 2022)

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. - Yue Guo 006; Felix Dietrich; Tom Bertalan; Danimir T. Doncevic; Manuel Dahmen; Ioannis G. Kevrekidis; Qianxiao Li (SIAM J. Sci. Comput., 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)

Software for "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction". - Artur M. Schweidtmann; Jan G. Rittig; Jana M. Weber; Martin Grohe; Manuel Dahmen; Kai Leonhard; Alexander Mitsos (Zenodo, 2023)

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. - Jan G. Rittig; Karim Ben Hicham; Artur M. Schweidtmann; Manuel Dahmen; Alexander Mitsos (Comput. Chem. Eng., 2023)

Demand response for flat nonlinear MIMO processes using dynamic ramping constraints. - Florian Joseph Baader; Philipp Althaus; André Bardow; Manuel Dahmen (Comput. Chem. Eng., 2023)

Physical pooling functions in graph neural networks for molecular property prediction. - Artur M. Schweidtmann; Jan G. Rittig; Jana M. Weber; Martin Grohe; Manuel Dahmen; Kai Leonhard; Alexander Mitsos (Comput. Chem. Eng., 2023)

Demand response scheduling of copper production under short-term electricity price uncertainty. - Sonja H. M. Germscheid; Fritz T. C. Röben; Han Sun; André Bardow; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2023)

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response. - Sonja H. M. Germscheid; Benedikt Nilges; Niklas von der Aßen; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2024)

End-to-end reinforcement learning of Koopman models for economic nonlinear model predictive control. - Daniel Mayfrank; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2024)

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization. - Daniel Mayfrank; Na-Young Ahn; Alexander Mitsos; Manuel Dahmen (CoRR, 2024)

A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. - Danimir T. Doncevic; Alexander Mitsos; Yue Guo 006; Qianxiao Li; Felix Dietrich; Manuel Dahmen; Ioannis G. Kevrekidis (SIAM J. Sci. Comput., 2024)

Sample-efficient reinforcement learning of Koopman eNMPC. - Daniel Mayfrank; Mehmet Velioglu; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2025)

Retrofitting Ordinary Portland cement production for reduced greenhouse gas emissions. - Ariana Y. Ojeda-Paredes; Alexander Mitsos; Manuel Dahmen (Comput. Chem. Eng., 2025)

Physics-informed neural networks for dynamic process operations with limited physical knowledge and data. - Mehmet Velioglu; Song Zhai; Sophia Rupprecht; Alexander Mitsos; Andreas Jupke; Manuel Dahmen (Comput. Chem. Eng., 2025)

Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic. - Hongzhen Zhang; Tim Kerkenhoff; Neil Kichler; Manuel Dahmen; Alexander Mitsos; Uwe Naumann; Dominik Bongartz (CoRR, 2025)