11 citations · 19 across the 16 of their papers we have counts for
9 papers · 1 filter
Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games
Georgios C. Chasparis
Reinforcement-based learning has attracted considerable attention both in modeling human behavior as well as in engineering, for designing measurement- or payoff-based optimization…
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…
Hourly Short Term Load Forecasting for Residential Buildings and Energy Communities
Aleksei Kychkin, Georgios C. Chasparis
Electricity load consumption may be extremely complex in terms of profile patterns, as it depends on a wide range of human factors, and it is often correlated with several exogenou…
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…