5 papers
Causally-Guided Pairwise Transformer -- Towards Foundational Digital Twins in Process Industry
Michael Mayr, Georgios C. Chasparis
Foundational modelling of multi-dimensional time-series data in industrial systems presents a central trade-off: channel-dependent (CD) models capture specific cross-variable dynam…
Causal Time-Series Synchronization for Multi-Dimensional Forecasting
Michael Mayr, Georgios C. Chasparis, Josef Küng
The process industry's high expectations for Digital Twins require modeling approaches that can generalize across tasks and diverse domains with potentially different data dimensio…
Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry
Michael Mayr, Georgios C. Chasparis, Josef Küng
Central to the digital transformation of the process industry are Digital Twins (DTs), virtual replicas of physical manufacturing systems that combine sensor data with sophisticate…
Automated Knowledge Graph Learning in Industrial Processes
Lolitta Ammann, Jorge Martinez-Gil, Michael Mayr +1
Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for auto…
Opening the black-box of Neighbor Embedding with Hotelling's T2 statistic and Q-residuals
Roman Josef Rainer, Michael Mayr, Johannes Himmelbauer +1
In contrast to classical techniques for exploratory analysis of high-dimensional data sets, such as principal component analysis (PCA), neighbor embedding (NE) techniques tend to b…