arXiv:2606. 16219v1 Announce Type: cross Abstract: Digital twin modeling, including control and data assimilation under model uncertainty, often faces an open-ended fidelity problem: adding variables, data streams, and time scales can indefinitely increase model complexity, ultimately producing systems that are difficult to maintain, validate, interpret, and use for stress or safety testing.
By Zongren Zou, Th\'eo Bourdais, Ricardo Baptista, Houman Owhadi
arXiv:2511. 00366v2 Announce Type: replace-cross Abstract: Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based models or surrogates.
By Shridhar Vashishtha, Krishna Prasath Logakannan, Jacob Hochhalter, Shandian Zhe, Robert M. Kirby
arXiv:2607. 03585v1 Announce Type: new Abstract: Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from heterogeneous and non-stationary time-series data.
By Quang Hung Pham, Ryad Zemouri, Martin Gagnon, Luc Vouligny
arXiv:2501. 08561v4 Announce Type: replace Abstract: Digital twins are increasingly used to monitor and optimize industrial systems, yet many existing frameworks remain difficult to interpret, slow to adapt, and limited in their ability to incorporate explicit domain knowledge.
By Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Houbing Herbert Song
arXiv:2601. 01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems.
By Mohammed Ayalew Belay, Adil Rasheed, Pierluigi Salvo Rossi
arXiv:2607. 00431v1 Announce Type: new Abstract: Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost.
By Md Rakibul Haque, Shireen Elhabian, Warren Woodrich Pettine