24 citations · 45 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach
Ammar N. Abbas, Shakra Mehak, Georgios C. Chasparis +4
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrate…
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…
AI-Powered Predictions for Electricity Load in Prosumer Communities
Aleksei Kychkin, Georgios C. Chasparis
The flexibility in electricity consumption and production in communities of residential buildings, including those with renewable energy sources and energy storage (a.k.a., prosume…
Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz +7
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and appl…