Applications of Digital Twins in Industry, Medicine, Environmental Sciences, Daily Life

RESEARCH TOPIC 4

A smarter and more sustainable Northeastern Italy through Digital Twins.

There are countless fields in which Digital Twins stand out for their efficiency and simulation speed. One notable example is the field of medicine, where time plays a critical role: traditional simulations of physiological processes, such as modeling the human heart and circulatory system, often require weeks of computation on high-performance supercomputers. However, with the integration of Digital Twins, this time frame can be reduced to just a few hours.
Research Topic 4 (RT4) aims to design and deploy demonstrative applications of Digital Twins, revolutionizing key sectors in Northeastern Italy: from port management to personalized medicine, from urban planning to environmental monitoring. These emerging technologies thus help foster a future in which digitalization plays a central role in driving sustainable development and societal well-being.

All news about RT4

Tasks and outputs

Research Topic 4 is organized into four tasks, each corresponding to a different step in the research workflow, from the review of digital twin applications to the development of use cases and their integration.

Each scientific output of Research Topic 4 is associated with a particular task and with a milestone that situates it within the iNEST project, either along the project timeline (2022, 2023, 2024, 2025) or within specific project activities (e.g., those involving Young Researcher Grants).

Copula-Based Conformal Anomaly Identification for Multivariate Time Series

Pearson, N. A., Zanello, F., Russo, D., Bortolussi, L., & Cairoli, F. (2025, September). CoCAI: Copula-Based Conformal Anomaly Identification for Multivariate Time-Series. In International Conference on Runtime Verification (pp. 296-316). Cham: Springer Nature Switzerland.

University of Trieste

2025

Conference paper

Deep Learning for Coastal Downscaling of Northern Adriatic Sea

Adobbati, F., Bonin, L., Cossarini, G., Di Biagio, V., Giordano, F., Manzoni, L., & Querin, S. (2025). A deep learning approach for coastal downscaling: The northern Adriatic Sea case-study. Ocean Modelling, 197, 102581.

OGS

2025

Journal paper

Deep Learning Super-Resolution Methods for Coastal Modeling

Adobbati, F., Bonin, L., Cossarini, G., Di Biagio, V., Giordano, F., Lazzari, P., Manzoni, L., Piani, S., Reale, M., Salon, S., Solidoro, C., & Querin, S. (2025). Applications of deep learning super-resolution methods for coastal ocean modelling. In Joint Proceedings of the Thematic Workshops at Ital-IA 2025 colocated with the 5th National Conference on Artificial Intelligence.

OGS

2025

Conference paper

1 – DT application review

Task RT4.1 consists of reviewing the latest applications of Digital Twins to the environment, daily life, and infrastructures.

2 – Use cases

Task RT4.2 aims at developing digital twin use cases for demonstration purposes in the industrial, medical, and environmental fields.

3 – Partner integration

Task RT4.3 involves the integration and extension of benchmarks and use cases within project partners.

4 – Final deployment

Task RT4.4 extends and integrates benchmarks and use cases to external partners, leading to the final deployment of digital twin applications.

Coordination

Research Topic 4 is led by the National Institute of Oceanography and Experimental Geophysics (OGS). The International School for Advanced Studies (SISSA), the University of Padua (UniPD), and the University of Trieste (UniTS) are also involved in RT4.