no code implementations • 21 Mar 2024 • Daniel Mayfrank, Na Young Ahn, Alexander Mitsos, Manuel Dahmen
We present a method for end-to-end learning of Koopman surrogate models for optimal performance in control.
no code implementations • 23 Nov 2023 • Hannes Hilger, Dirk Witthaut, Manuel Dahmen, Leonardo Rydin Gorjao, Julius Trebbien, Eike Cramer
Additionally, our analysis highlights how our improvements towards adaptations in changing regimes allow the normalizing flow to adapt to changing market conditions and enable continued sampling of high-quality day-ahead price scenarios.
no code implementations • 3 Aug 2023 • Daniel Mayfrank, Alexander Mitsos, Manuel Dahmen
(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic models that are sufficiently accurate and computationally tractable.
no code implementations • 22 Nov 2022 • Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis
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.
no code implementations • 27 Jul 2022 • Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos
We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent.
no code implementations • 25 Jul 2022 • Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design.
no code implementations • 23 Jun 2022 • Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos
We train the GNN on a database including more than 40, 000 AC values and compare it to a state-of-the-art MCM.
no code implementations • 1 Jun 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
We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space.
no code implementations • 27 May 2022 • Eike Cramer, Dirk Witthaut, Alexander Mitsos, Manuel Dahmen
This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts.
no code implementations • 5 Apr 2022 • Eike Cramer, Leonard Paeleke, Alexander Mitsos, Manuel Dahmen
We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems.
no code implementations • 8 Mar 2022 • Eike Cramer, Felix Rauh, Alexander Mitsos, Raúl Tempone, Manuel Dahmen
To model manifold data using normalizing flows, we employ isometric autoencoders to design embeddings with explicit inverses that do not distort the probability distribution.
no code implementations • 27 Oct 2021 • Eike Cramer, Leonardo Rydin Gorjão, Alexander Mitsos, Benjamin Schäfer, Dirk Witthaut, Manuel Dahmen
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 electricity prices.
2 code implementations • 4 May 2021 • Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li
As a case study, we develop a machine learning approach that automatically learns effective solvers for initial value problems in the form of ordinary differential equations (ODEs), based on the Runge-Kutta (RK) integrator architecture.
no code implementations • 21 Apr 2021 • Eike Cramer, Alexander Mitsos, Raul Tempone, Manuel Dahmen
We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.