Manuel Dahmen

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 29

Top Topics

Molecular Structure

Nonlinear Manifold Learning

Production Processes

Top Venues

Comput. Chem. Eng.

SIAM J. Sci. Comput.

Publications

Daniel Mayfrank, Alexander Mitsos, Manuel Dahmen
End-to-end reinforcement learning of Koopman models for economic nonlinear model predictive control. Comput. Chem. Eng. 190 (2024)
Sonja H. M. Germscheid, Benedikt Nilges, Niklas von der Aßen, Alexander Mitsos, Manuel Dahmen
Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response. Comput. Chem. Eng. 185 (2024)
Daniel Mayfrank, Na-Young Ahn, Alexander Mitsos, Manuel Dahmen
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization. CoRR (2024)
Mehmet Velioglu, Song Zhai, Sophia Rupprecht, Alexander Mitsos, Andreas Jupke, Manuel Dahmen
Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data. CoRR (2024)
Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. SIAM J. Sci. Comput. 46 (2) (2024)
Sonja H. M. Germscheid, Fritz T. C. Röben, Han Sun, André Bardow, Alexander Mitsos, Manuel Dahmen
Demand response scheduling of copper production under short-term electricity price uncertainty. Comput. Chem. Eng. 178 (2023)
Daniel Mayfrank, Alexander Mitsos, Manuel Dahmen
End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control. CoRR (2023)
Hannes Hilger, Dirk Witthaut, Manuel Dahmen, Leonardo Rydin Gorjão, Julius Trebbien, Eike Cramer
Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows. CoRR (2023)
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos
Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. Comput. Chem. Eng. 171 (2023)
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos
Physical pooling functions in graph neural networks for molecular property prediction. Comput. Chem. Eng. 172 (2023)
Florian Joseph Baader, Philipp Althaus, André Bardow, Manuel Dahmen
Demand response for flat nonlinear MIMO processes using dynamic ramping constraints. Comput. Chem. Eng. 172 (2023)
Eike Cramer, Felix Rauh, Alexander Mitsos, Raúl Tempone, Manuel Dahmen
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows. CoRR (2022)
Eike Cramer, Leonard Paeleke, Alexander Mitsos, Manuel Dahmen
Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators. Comput. Chem. Eng. 166 (2022)
Eike Cramer, Leonard Paeleke, Alexander Mitsos, Manuel Dahmen
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators. CoRR (2022)
Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. CoRR (2022)
Eike Cramer, Dirk Witthaut, Alexander Mitsos, Manuel Dahmen
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows. CoRR (2022)
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids. CoRR (2022)
Philipp Glücker, Marco Langiu, Thiemo Pesch, Manuel Dahmen, Andrea Benigni
Incorporating AC Power Flow into the Multi-Energy System Optimization Framework COMANDO. OSMSES (2022)
Eike Cramer, Leonardo Rydin Gorjão, Alexander Mitsos, Benjamin Schäfer, Dirk Witthaut, Manuel Dahmen
Validation Methods for Energy Time Series Scenarios From Deep Generative Models. IEEE Access 10 (2022)
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction. CoRR (2022)
Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. SIAM J. Sci. Comput. 44 (4) (2022)
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, K. Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen
Graph Machine Learning for Design of High-Octane Fuels. CoRR (2022)
Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann
Graph neural networks for the prediction of molecular structure-property relationships. CoRR (2022)
Marco Langiu, Manuel Dahmen, Alexander Mitsos
Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points. Comput. Chem. Eng. 161 (2022)
Eike Cramer, Alexander Mitsos, Raúl Tempone, Manuel Dahmen
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows. CoRR (2021)
Eike Cramer, Leonardo Rydin Gorjão, Alexander Mitsos, Benjamin Schäfer, Dirk Witthaut, Manuel Dahmen
Validation Methods for Energy Time Series Scenarios from Deep Generative Models. CoRR (2021)
Marco Langiu, David Yang Shu, Florian Joseph Baader, Dominik Hering, Uwe Bau, André Xhonneux, Dirk Müller, André Bardow, Alexander Mitsos, Manuel Dahmen
COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization. Comput. Chem. Eng. 152 (2021)
Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li
Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators. CoRR (2021)
Andrea König, Lisa Neidhardt, Jörn Viell, Alexander Mitsos, Manuel Dahmen
Integrated design of processes and products: Optimal renewable fuels. Comput. Chem. Eng. 134 (2020)