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Found 9 papers, 0 papers with code
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
We train the GNN on a database including more than 40, 000 AC values and compare it to a state-of-the-art MCM.
Graph Machine Learning for Design of High-Octane Fuels
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
In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern.
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators
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
To model manifold data using normalizing flows, we propose to employ the isometric autoencoder to design nonlinear encodings with explicit inverses.
Validation Methods for Energy Time Series Scenarios from Deep Generative Models
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.
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows
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.
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
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.
Global Optimization of Gaussian processes
Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems.
