MAIN PROJECT OBJECTIVE
Initiative funded by the MUR — Italian Ministry of University and Research, Decree no. 307 of 18/03/2025, to support initiatives strengthening strategic research chains, networking aggregation forms among research stakeholders, and skills development for smart specialisation, industrial transition and entrepreneurship. Action 1.1.2 — Support for a limited number of strategic research chains (RISE); Action 1.1.3b — Support for validating and networking aggregation forms that help cross-fertilise the research system (NET).
 
DESCRIPTION
Development of methodologies, models, algorithms and techniques for building intelligent digital twin ecosystems (DTSE), based on a distributed Edge-Cloud-HPC processing model that balances computational loads, reduces latency and improves resilience and privacy.
 

CUP: B89H26000160005 (RISE) — B82F26000140005 (NET) 
TOTAL AMOUNT: n/a
FUNDED AMOUNT: € 91.500,00 (RISE) — € 115.000,00 (NET)

PROJECT START: 05/05/2026
EXPECTED END: n/a

CONTACT
[email protected] | www.netservice.eu

Echo-Twin makes digital twin ecosystems smarter through distributed Edge-Cloud-HPC processing.

Echo-Twin is a project funded by the MUR to support the strengthening of strategic research chains, the networking of aggregation forms among research stakeholders, and skills development for smart specialisation and industrial transition.


The project aims to develop methodologies, models, algorithms and techniques for building intelligent digital-twin-based ecosystems capable of balancing computational loads, reducing latency and improving resilience and privacy.

A distributed Edge-Cloud-HPC model for digital twins

Echo-Twin develops methodologies, models, algorithms and techniques for building intelligent ecosystems based on digital twins (Digital Twin System Ecosystems, DTSE), based on a distributed processing model that integrates edge computing, cloud and high-performance computing (HPC), enabling computational loads to be distributed as efficiently as possible while reducing latency and improving resilience and data protection.

Technologies and application areas

Digital Twin System Ecosystems (DTSE)

Intelligent ecosystems based on digital twins for modelling and simulating complex.

Distributed Edge-Cloud-HPC processing
Balances computational loads across edge devices, cloud infrastructures and high-performance computing resources.
 
Workflow-enhanced Inference Networks
Inference networks enhanced by distributed workflows, designed to reduce latency and improve resilience and privacy. 
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