Digital Twin and Foundation Models: A New Frontier

Athanasios Trantas, Paolo Pileggi

2024

Abstract

A Foundation Model (FM) possesses extensive learning capabilities; it learns from diverse datasets. This is our opportunity to enhance the functionality of Digital Twin (DT) solutions in various sectors. The integration of FMs into the DT application is particularly relevant due to the increased prevalence of Artificial Intelligence (AI) in real-world applications. In this position paper, we begin to explain a novel perspective on this integration by exploring the potential of enhanced predictive analytics, adaptive learning, and improved handling of complex data within DTs — by way of designated purposes. Ultimately, we aim to uncover hidden value of enhanced reliable decision-making, whereby systems can make more informed, accurate and timely decisions, based on comprehensive data analytics and predictive insights. Mentioning selected ongoing cases, we highlight some benefits and challenges, like computational demand, data privacy concerns, and the need for transparency in AI decision-making. Underscoring the transformative implications of integrating FMs into the DT paradigm, a shift towards more intelligent, versatile and dynamic systems becomes clearer. We caution against the challenges of computational resources, safety considerations and interpretability. This step is pivotal towards unlocking unprecedented potential for advanced data-driven solutions in various industries.

Paper Citation

in Harvard Style

Trantas A. and Pileggi P. (2024). Digital Twin and Foundation Models: A New Frontier. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 988-994. DOI: 10.5220/0012427000003636

in Bibtex Style

@conference{icaart24,
author={Athanasios Trantas and Paolo Pileggi},
title={Digital Twin and Foundation Models: A New Frontier},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={988-994},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012427000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Digital Twin and Foundation Models: A New Frontier
SN - 978-989-758-680-4
AU - Trantas A.
AU - Pileggi P.
PY - 2024
SP - 988
EP - 994
DO - 10.5220/0012427000003636
PB - SciTePress