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This study introduces and compares the Hankel dynamic mode decomposition with control (Hankel-DMDc) and a novel Bayesian extension of Hankel-DMDc as model-free (i.e., data-driven and equation-free) approaches for system identification and…

Systems and Control · Electrical Eng. & Systems 2025-02-25 Giorgio Palma , Andrea Serani , Shawn Aram , David W. Wundrow , David Drazen , Matteo Diez

A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to update and derive an interpretable expression for the digital…

Machine Learning · Statistics 2022-12-20 Tapas Tripura , Aarya Sheetal Desai , Sondipon Adhikari , Souvik Chakraborty

In order to guarantee the safety of payload, crew, and structures, ships must exhibit good seakeeping, maneuverability, and structural-response performance, also when they operate in adverse weather conditions. In this context, the…

Dynamical Systems · Mathematics 2022-11-08 Andrea Serani , Paolo Dragone , Frederick Stern , Matteo Diez

A data-driven and equation-free approach is proposed and discussed to model ships maneuvers in waves, based on the dynamic mode decomposition (DMD). DMD is a dimensionality-reduction/reduced-order modeling method, which provides a linear…

Dynamical Systems · Mathematics 2021-05-28 Matteo Diez , Andea Serani , Emilio F. Campana , Frederick Stern

A digital twin is a surrogate model that has the main feature to mirror the original process behavior. Associating the dynamical process with a digital twin model of reduced complexity has the significant advantage to map the dynamics with…

Numerical Analysis · Mathematics 2024-03-19 Diana Alina Bistrian , Omer San , Ionel Michael Navon

As the global population grows and climate change intensifies, sustainable food production is critical. Marine aquaculture offers a viable solution, providing a sustainable protein source. However, the industry's expansion requires novel…

Machine Learning · Computer Science 2024-06-11 Eirini Katsidoniotaki , Biao Su , Eleni Kelasidi , Themistoklis P. Sapsis

While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature.…

Systems and Control · Electrical Eng. & Systems 2023-01-24 Federico Zocco , Hsueh-Cheng Wang , Mien Van

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system…

Numerical Analysis · Mathematics 2023-11-10 Matteo Torzoni , Marco Tezzele , Stefano Mariani , Andrea Manzoni , Karen E. Willcox

This work shows how adaptivity can enhance value realization of digital twins in civil engineering. We focus on adapting the state transition models within digital twins represented through probabilistic graphical models. The bi-directional…

Machine Learning · Computer Science 2026-05-12 Eugenio Varetti , Matteo Torzoni , Marco Tezzele , Andrea Manzoni

Simulation-based digital twins must provide accurate, robust and reliable digital representations of their physical counterparts. Quantifying the uncertainty in their predictions plays, therefore, a key role in making better-informed…

Computational Engineering, Finance, and Science · Computer Science 2024-10-14 Daniel Andrés Arcones , Martin Weiser , Phaedon-Stelios Koutsourelakis , Jörg F. Unger

This paper explores the development and practical application of a predictive digital twin specifically designed for condition monitoring, using advanced mathematical models and thermal imaging techniques. Our work presents a comprehensive…

Computer Vision and Pattern Recognition · Computer Science 2024-11-12 Daniel Menges , Florian Stadtmann , Henrik Jordheim , Adil Rasheed

We present a Bayesian inversion-based digital twin that employs acoustic pressure data from seafloor sensors, along with 3D coupled acoustic-gravity wave equations, to infer earthquake-induced spatiotemporal seafloor motion in real time and…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-12-09 Stefan Henneking , Sreeram Venkat , Veselin Dobrev , John Camier , Tzanio Kolev , Milinda Fernando , Alice-Agnes Gabriel , Omar Ghattas

The demand for condition-based and predictive maintenance is rising across industries, especially for remote, high-value, and high-risk assets. In this article, the diagnostic digital twin concept is introduced, discussed, and implemented…

Machine Learning · Computer Science 2024-06-06 Florian Stadtmann , Adil Rasheed

The concept of creating a virtual copy of a complete Cyber-Physical System opens up numerous possibilities, including real-time assessments of the physical environment and continuous learning from the system to provide reliable and precise…

Artificial Intelligence · Computer Science 2023-11-22 Carine Menezes Rebello , Johannes Jäschkea , Idelfonso B. R. Nogueira

The real-time supervision of production processes is a common challenge across several industries. It targets process component monitoring and its predictive maintenance in order to ensure safety, uninterrupted production and maintain high…

Machine Learning · Computer Science 2026-02-27 Osimone Imhogiemhe , Yoann Jus , Hubert Lejeune , Saïd Moussaoui

We live in a world of exploding complexity driven by technical evolution as well as highly volatile socio-economic environments. Managing complexity is a key issue in everyday decision making such as providing safe, sustainable, and…

Computers and Society · Computer Science 2023-11-28 Dirk Hartmann

This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). All the analyses are performed on experimental data…

Machine Learning · Computer Science 2025-02-18 Giorgio Palma , Andrea Bardazzi , Alessia Lucarelli , Chiara Pilloton , Andrea Serani , Claudio Lugni , Matteo Diez

Digital twinning in structural engineering is a rapidly evolving technology that aims to eliminate the gap between physical systems and their digital models through real-time sensing, visualization, and control techniques. Although Digital…

Applications · Statistics 2024-08-28 Zeyu Wang , Ziqi Wang

Digital Twins bring several benefits for planning, operation, and maintenance of remote offshore assets. In this work, we explain the digital twin concept and the capability level scale in the context of wind energy. Furthermore, we…

Signal Processing · Electrical Eng. & Systems 2023-04-04 Florian Stadtmann , Henrik Gusdal Wassertheurer , Adil Rasheed

Digital twins have attracted a great deal of recent attention from a wide range of fields. A basic requirement for digital twins of nonlinear dynamical systems is the ability to generate the system evolution and predict potentially…

Machine Learning · Computer Science 2023-09-21 Ying-Cheng Lai
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