Deep Anomaly Detection on Tennessee Eastman Process Data

no code implementations • 10 Mar 2023 • Fabian Hartung, Billy Joe Franks, Tobias Michels, Dennis Wagner, Philipp Liznerski, Steffen Reithermann, Sophie Fellenz, Fabian Jirasek, Maja Rudolph, Daniel Neider, Heike Leitte, Chen Song, Benjamin Kloepper, Stephan Mandt, Michael Bortz, Jakob Burger, Hans Hasse, Marius Kloft

Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.

Chemical Process Unsupervised Anomaly Detection

Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties

no code implementations • 17 Feb 2022 • Fabian Jirasek, Robert Bamler, Stephan Mandt

We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the data-driven and physical baselines and established ensemble methods from the machine learning literature.

Bayesian Inference BIG-bench Machine Learning

Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data

no code implementations • 28 Jul 2021 • Jan-Tobias Sohns, Michaela Schmitt, Fabian Jirasek, Hans Hasse, Heike Leitte

Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information.

Attribute Matrix Completion +1

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

no code implementations • 29 Jan 2020 • Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay, Robert A. Vandermeulen, Robert Bamler, Michael Bortz, Stephan Mandt, Marius Kloft, Hans Hasse

Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes.

BIG-bench Machine Learning Matrix Completion