interTwin 2nd Technical Meeting
On Thursday, June 22nd and Friday, June 23rd – 35 interTwin project partners got together during #EGI2023 in Poznań, Poland for the 2nd technical project meeting.
The Thursday meeting covered the activities in WP3 (Technical Coordination and Interoperability), the Blueprint Architecture and featured a presentation about DestinE (Destination Earth) linked activities by Thomas Geenen (who also gave a keynote presentation about DestinE at the main conference on Wednesday). Both presentations received interesting insights from the experts in the External Advisory Board.
Meeting continued with the current technical achievements of the project in the area of Digital Twin Engine Infrastructure and Digital Twin Engine Core Modules, as well as the next steps to take.
On Friday morning, the meeting discussed the status and the plans for the interTwin thematic modules and Digital Twins Applications in the area of Climate change Future Projections , Early Warning and Impact of for Extreme Events, High Energy Physics and Radio Astronomy,
The project is entering the last phase of its first year, which is focusing on the preparation of the First technological release to be delivered in Fall 2023, therefore the plans for Software release and integration have been also discussed.
The project meeting has highlighted the area of focus for the next period and has strengthened the collaboration between the User communities and the technological and resource provider partners which are working together towards a Co-designed Digital Twin Engine.
On Wednesday, June 21st, Andrea Manzi (interTwin Technical Coordinator, EGI) presented interTwin in the session: Data analytics platforms, tools and VREs for EOSC
The talk highlighted the use cases supported, the first DTE architecture that has been finalised and the relation with existing initiatives like Destination Earth and EOSC.
Donatello Elia (CMCC) presented the poster ‘A digital twin engine for extreme weather events analysis on climate projections in the interTwin project’
Climate Change has been leading to an exacerbation of Extreme Weather Events (EWEs), such as storms and wildfires, raising major concerns in terms of their increase of their intensity, frequency and duration. Detecting and predicting extreme events is challenging due to the rare occurrence of these events and consequently the lack of related historical data.
Machine Learning (ML) approaches represent emerging solutions for dealing with extreme events analysis, providing cost-effective and fast-computing methods that can complement or replace traditional methodologies. Such solutions require huge amounts of heterogeneous data for properly training and running the models, which in turn pose big challenges in terms of data management, computing/memory resource requirements, workflow orchestration and software infrastructure
needs. A Digital Twin for EWEs integrating data and models could provide scientists and policy makers with a system for conducting prompt analysis and evaluating what-if scenarios.
In the context of the EU-funded interTwin project, a Digital Twin for the analysis of extreme events, targeting tropical cyclones and wildfires, on future climate projections following a data-driven approach is being developed. The interTwin project aims at defining a Digital Twin Engine for supporting scientific Digital Twins applications from different fields. This contribution will present the initial work behind the design of this machine learning-powered Digital Twin for extreme events
studies as well as some preliminary results.