Zobrazeno 1 - 10
of 2 242
pro vyhledávání: '"Mitsos, A"'
Motivated by the increasing need to hedge against load and generation uncertainty in the operation of power grids, we propose flexibility maximization during operation. We consider flexibility explicitly as the amount of uncertainty that can be handl
Externí odkaz:
http://arxiv.org/abs/2411.18178
Time-series information needs to be incorporated into energy system optimization to account for the uncertainty of renewable energy sources. Typically, time-series aggregation methods are used to reduce historical data to a few representative scenari
Externí odkaz:
http://arxiv.org/abs/2411.14320
Autor:
Brozos, Christoforos, Rittig, Jan G., Akanny, Elie, Bhattacharya, Sandip, Kohlmann, Christina, Mitsos, Alexander
Surfactants are key ingredients in foaming and cleansing products across various industries such as personal and home care, industrial cleaning, and more, with the critical micelle concentration (CMC) being of major interest. Predictive models for CM
Externí odkaz:
http://arxiv.org/abs/2411.02224
Autor:
Pirnay, Jonathan, Rittig, Jan G., Wolf, Alexander B., Grohe, Martin, Burger, Jakob, Mitsos, Alexander, Grimm, Dominik G.
Generative deep learning has become pivotal in molecular design for drug discovery and materials science. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on
Externí odkaz:
http://arxiv.org/abs/2411.01667
Autor:
Rittig, Jan G., Mitsos, Alexander
We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture ensures thermodynamic consistency by predicting the molar excess Gibbs free ene
Externí odkaz:
http://arxiv.org/abs/2407.18372
Autor:
Velioglu, Mehmet, Zhai, Song, Rupprecht, Sophia, Mitsos, Alexander, Jupke, Andreas, Dahmen, Manuel
In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for modeling dynamic processes with incomplete mechanistic semi-explici
Externí odkaz:
http://arxiv.org/abs/2406.01528
We propose a method to construct representative price profiles of the day-ahead (DA) and the intraday (ID) electricity spot markets and use this method to provide examples of ready-to-use price data sets. In contrast to common scenario generation app
Externí odkaz:
http://arxiv.org/abs/2405.14403
We present a method for end-to-end learning of Koopman surrogate models for optimal performance in a specific control task. In contrast to previous contributions that employ standard reinforcement learning (RL) algorithms, we use a training algorithm
Externí odkaz:
http://arxiv.org/abs/2403.14425
Autor:
Koronaki, Eleni D., Kaven, Luise F., Faust, Johannes M. M., Kevrekidis, Ioannis G., Mitsos, Alexander
Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in-line concentration monitoring. Recent approaches and some theoreti
Externí odkaz:
http://arxiv.org/abs/2403.08376
Autor:
Brozos, Christoforos, Rittig, Jan G., Bhattacharya, Sandip, Akanny, Elie, Kohlmann, Christina, Mitsos, Alexander
The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in industry. Recently, classical QSPR and Graph Neural Networks (GNNs), a deep learning technique, have been successfully applied to
Externí odkaz:
http://arxiv.org/abs/2403.03767