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cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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…