29 Jul/24
13:30 - 15:30 (Europe/Zurich)

Digital Twins: introduction and use cases


31/3-004 at CERN


interTwin is an EC-funded project that seeks to harness the potential of 'Digital Twins' in a diverse range of scientific fields within earth observation and physics. The project's core modules offer essential capabilities for the development and management of data-driven and compute-intensive applications. These capabilities include workflow composition, data fusion, AI workflow and method lifecycle management, real-time acquisition and data analytics, as well as validation, verification, and uncertainty tracing to ensure model quality. A key focus of interTwin is to establish seamless communication and interoperability among High Performance Computing (HPC), High Throughput Computing (HTC), and cloud resource providers. The project aims to establish consistent security measures, access policies, and resource accounting mechanisms to simplify resource access across different computing infrastructures. By doing so, interTwin aims to facilitate efficient and effective resource utilization for the advancement of scientific research and development in earth observation and physics.



Alexander Zoechbauer graduated with a MSc in Information Technology from ETH Zürich. Afterwards, he worked at the European Space Agency developing models for 3D asteroid surface reconstruction and computer vision algorithms for the HERA mission. Now, he is a Fellow at the CERN openlab working on InterTwin – an interdisciplinary Digital Twin Engine for Science. His research interest is especially focused on the intersection of computer science with other scientific disciplines, such as nanophotonics, transportation, aerospace and high-energy physics.

Kalliopi Tsolaki received a BSc on Mathematics from the Aegean University in Greece. She also holds a MSc degree οn Digital Media & Computational Intelligence from the Department of Informatics at Aristotle University of Thessaloniki. Since September 2022 Kalliopi works at CERN as an IT fellow having the role of a Data Scientist, contributing on projects involving Machine Learning applications in Physics. Prior joining CERN she worked in IT research, as well as in the consulting industry. She is currently involved with the development of a digital twin for particle detector simulations leveraging ML, in the framework of interTwin project. interTwin is an innovative project that builds a Digital Twin Engine incorporating a variety of digital twin applications from the physics and environmental domains.