Digital Twins for a Sustainable Economy
In today’s industrial world, efficiency and sustainability are paramount. To enhance productivity and reduce resource waste, industries can leverage the immense power of Digital Twins—virtual replicas of physical systems that integrate AI for real-time monitoring, simulation, and the optimization of processes and products.
This course offers an in-depth understanding of Digital Twins (DTs), covering theoretical foundations, case studies, and practical applications. Participants will gain valuable knowledge to apply in real-world contexts, hence fostering a sustainable industrial future.
Target
The course is specifically designed for companies that wish to innovate their production processes and improve operational efficiency through the use of Digital Twins.
What we offer
- Innovative content: the course will cover real case studies, advanced methodologies, and practical implementation techniques of Digital Twins in various industrial sectors.
- Expert instructors: the course will be led by researchers and University Professors with recognized experience in the field of Digital Twins and related technologies.
- Diverse expertise: the course features a cross-cutting and multidisciplinary approach, aligned with the diverse specializations of the involved research centers.
Why participate
- Competitive advantage: deepening your knowledge of Digital Twins can give your company a significant edge over competitors.
- Practical integration: by the end of the course, you will be able to seamlessly integrate Digital Twins within your business context.
- Networking: participants will share experiences and challenges with other companies and professionals who are implementing these technologies.
Course details
Objectives
In this course, you will:
- Learn the fundamentals of real-time physical simulation techniques and Digital Twins.
- Understand predictive monitoring and optimal control with AI algorithms and DTs.
- Develop skills in scientific computing by applying simulation techniques in a case study.
- Discover how DTs are applied to marine environments, focusing on the North Adriatic Sea.
- Gain knowledge on further industrial and environmental applications of Digital Twins.
Schedule
The course consists of 5 afternoon sessions, for a total amount of 20 hours of training.
Sessions will take place in five weeks between September and November, from 1:30 to 6:00 p.m. A coffee break, included in the registration fee, will be provided between 3:30 p.m. and 4:00 p.m.
Before session 1 and session 5 you are invited to a light lunch, included in the registration fee, at the course venue, from 12:30 to 1:30 p.m.
For sessions 2, 3, and 4, you will also be able to participate via videoconference, through an online streaming platform.
| Session | Date | Mode | |
|---|---|---|---|
| Introduction to Digital Twins and Physical Modeling | |||
| Data-Driven Modeling for Digital Twins: Simulation and AI | |||
| Case Studies of Industrial Digital Twins | |||
| Digital Twins of Marine Environments | |||
| Interactive parallel session |
The full program of each session can be read in the course brochure.
Venue
The first four sessions of the course (30th September, 7th October, 14th October, 21st October) will be held in the Sala Maggiore of the Venezia Giulia Chamber of Commerce (CCIAA) in Trieste, piazza della Borsa No. 14.
The fifth and final session (4th November) will be held in the Big Meeting Room (7th floor) of SISSA in Trieste, via Bonomea No. 265.
Organizers
The course is organized by SISSA, International School for Advanced Studies, in collaboration with some leading research centers in the Triveneto region: the University of Trieste, the University of Padua, and OGS (National Institute of Oceanography and Experimental Geophysics). It is an initiative promoted by the iNEST (Interconnected Northeast Innovation Ecosystem) Consortium.
Instructors
From SISSA:
Andrea Cangiani
Nicola Demo
Gianluigi Rozza
From the University of Padua:
Antonia Larese
Mario Putti
Fabio Marcuzzi
From the University of Trieste:
Francesca Cairoli
Erica Salvato
From OGS:
Stefano Salon
Gianpiero Cossarini
Stefano Querin
Federica Adobbati
Stefano Campanella
How to register
You can sign up for the course on the Indico platform.
Registration requires payment of a €200 fee per company.
Registration will be open until 22nd September 2024.
You can ask for more information to Prof. Andrea Cangiani: acangian@sissa.it.
Important: The course will be activated upon reaching a minimum amount of 10 registrations.
Books, papers, websites
Liu et al. Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems 58:B, 346-361, 2021.
Brunton & Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2022.
Hesthaven et al. Certified Reduced Basis Methods for Parametrized Partial Differential Equations. Springer, 2015.
A. Thelen et al. A comprehensive review of digital twin – part 1. Structural and Multidisciplinary Optimization 65:354, 2022.
Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.
Salvato, Erica, et al. “Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning.” IEEE Access 9 (2021): 153171-153187.
WHEN
30th September – 4th November 2024
WHERE
CCIAA Venezia Giulia, piazza della Borsa no. 14, Trieste (sessions 1 – 4)
SISSA, via Bonomea no. 265, Trieste (session 5)
NUMBER OF PARTICIPANTS
10 – 90 participants
TRAINING HOURS
20 hours
LANGUAGE
Italian
COST
€200 per company
PARTICIPATION CERTIFICATE
Available
