196 citations · 723 across the 27 of their papers we have counts for
10 papers · 1 filter
End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
Daniel Mayfrank, Alexander Mitsos, Manuel Dahmen
(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic models that are sufficiently accurate and computationally tractable. Data-driven surrogate models for mecha…
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
Danimir T. Doncevic, Alexander Mitsos, Yue Guo +4
Meta-learning of numerical algorithms for a given task consists of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters. To…
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber +4
Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the…
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann +2
Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MC…
Graph Machine Learning for Design of High-Octane Fuels
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann +7
Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition p…
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows
Eike Cramer, Dirk Witthaut, Alexander Mitsos +1
Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of r…
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows
Eike Cramer, Felix Rauh, Alexander Mitsos +2
To model manifold data using normalizing flows, we employ isometric autoencoders to design embeddings with explicit inverses that do not distort the probability distribution. Using…
Validation Methods for Energy Time Series Scenarios from Deep Generative Models
Eike Cramer, Leonardo Rydin Gorjão, Alexander Mitsos +3
The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electr…
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows
Eike Cramer, Alexander Mitsos, Raul Tempone +1
Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources such as photovoltaics (PV) and wind as well as load demands has…
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
Simon Olofsson, Eduardo S. Schultz, Adel Mhamdi +3
Diverse domains of science and engineering use parameterised mechanistic models. Engineers and scientists can often hypothesise several rival models to explain a specific process o…