Manuel Dahmen

Orcid: 0000-0003-2757-5253

According to our database1, Manuel Dahmen authored at least 24 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms.
SIAM J. Sci. Comput., 2024

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data.
CoRR, 2024

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization.
CoRR, 2024

End-to-end reinforcement learning of Koopman models for economic nonlinear model predictive control.
Comput. Chem. Eng., 2024

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response.
Comput. Chem. Eng., 2024

2023
Demand response scheduling of copper production under short-term electricity price uncertainty.
Comput. Chem. Eng., October, 2023

Physical pooling functions in graph neural networks for molecular property prediction.
Comput. Chem. Eng., April, 2023

Demand response for flat nonlinear MIMO processes using dynamic ramping constraints.
Comput. Chem. Eng., April, 2023

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids.
Comput. Chem. Eng., March, 2023

Software for "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction".
Dataset, January, 2023

Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows.
CoRR, 2023

2022
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators.
SIAM J. Sci. Comput., 2022

Graph neural networks for the prediction of molecular structure-property relationships.
CoRR, 2022

Graph Machine Learning for Design of High-Octane Fuels.
CoRR, 2022

Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows.
CoRR, 2022

Nonlinear Isometric Manifold Learning for Injective Normalizing Flows.
CoRR, 2022

Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points.
Comput. Chem. Eng., 2022

Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators.
Comput. Chem. Eng., 2022

Validation Methods for Energy Time Series Scenarios From Deep Generative Models.
IEEE Access, 2022

Incorporating AC Power Flow into the Multi-Energy System Optimization Framework COMANDO.
Proceedings of the Open Source Modelling and Simulation of Energy Systems, 2022

2021
Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators.
CoRR, 2021

Principal Component Density Estimation for Scenario Generation Using Normalizing Flows.
CoRR, 2021

COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization.
Comput. Chem. Eng., 2021

2020
Integrated design of processes and products: Optimal renewable fuels.
Comput. Chem. Eng., 2020


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