19 citations · 47 across the 13 of their papers we have counts for
5 papers · 1 filter
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Mayank Nagda, Phil Ostheimer, Justus Arweiler +13
Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning mo…
Thermodynamically consistent machine learning model for excess Gibbs energy
Marco Hoffmann, Thomas Specht, Quirin Göttl +4
The excess Gibbs energy plays a central role in chemical engineering and chemistry, providing a basis for modeling thermodynamic properties of liquid mixtures. Predicting the exces…
GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures
Marco Hoffmann, Hans Hasse, Fabian Jirasek
Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source p…
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures
Dominik Gond, Jan-Tobias Sohns, Heike Leitte +2
Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing att…