Search Results for author: Alexander Mitsos

Found 9 papers, 0 papers with code

Graph Machine Learning for Design of High-Octane Fuels

no code implementations1 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.

Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows

no code implementations27 May 2022 Eike Cramer, Dirk Witthaut, Alexander Mitsos, Manuel Dahmen

In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern.

Density Estimation Prediction Intervals

Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators

no code implementations5 Apr 2022 Eike Cramer, Leonard Paeleke, Alexander Mitsos, Manuel Dahmen

In the stochastic day-ahead bidding problem, the conditional scenarios from all methods lead to significantly more profitable and reliable results compared to an unconditional selection of historical scenarios.

Nonlinear Isometric Manifold Learning for Injective Normalizing Flows

no code implementations8 Mar 2022 Eike Cramer, Felix Rauh, Alexander Mitsos, Raúl Tempone, Manuel Dahmen

To model manifold data using normalizing flows, we propose to employ the isometric autoencoder to design nonlinear encodings with explicit inverses.

Density Estimation

Validation Methods for Energy Time Series Scenarios from Deep Generative Models

no code implementations27 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.

Time Series

Principal Component Density Estimation for Scenario Generation Using Normalizing Flows

no code implementations21 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.

Density Estimation Image Generation +1

Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty

no code implementations7 Feb 2021 Simon Olofsson, Eduardo S. Schultz, Adel Mhamdi, Alexander Mitsos, Marc Peter Deisenroth, Ruth Misener

Typically, several rival mechanistic models can explain the available data, so design of dynamic experiments for model discrimination helps optimally collect additional data by finding experimental settings that maximise model prediction divergence.

Gaussian Processes

Global Optimization of Gaussian processes

no code implementations21 May 2020 Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, Alexander Mitsos

Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems.

Gaussian Processes

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