Speaker: Dimitrios Loukrezis
Title: Scientific Machine Learning and Uncertainty Quantification for Predictive Digital Twins
The Digital Twin (DT) is a cornerstone technology concept within the ongoing 4th industrial revolution. By connecting physical assets to their digital replicas, DTs offer unprecedented opportunities for innovation in industrial design and operation, for example, with respect to design optimization, decision support, and real-time monitoring. State of the art computational methods utilized within DT applications combine approaches stemming from traditional, physics-based computational science and engineering (CSE), with data-driven, machine learning (ML)-based techniques. This combination, often referred to as scientific machine learning (SciML), is widely considered to be the next step in the evolution of CSE and receives much attention within both industry and academia. At the same time, the models employed in a DT, physics-based, data-driven, or hybrid, will to some extent deviate from the corresponding real-world systems, commonly due to aleatory or epistemic uncertainties (or a mix thereof). In this context, uncertainty quantification (UQ) plays a crucial role in accompanying the DT's predictions with uncertainty metrics, thus providing robustness and reliability estimates. This talk will (i) present a general overview of DTs; (ii) present specific use-cases of SciML and UQ for DT applications; (iii) discuss currently missing aspects for realizing holistic DTs, i.e., data-driven, physics-conforming, and uncertainty-aware.
