The paper introduces Shallow Recurrent Decoder (SHRED) networks as a data‑driven method for accurate state estimation in engineering systems, specifically applied to the TRIGA Mark II research reactor. SHRED maps sparse sensor measurements to the full state space, handling noisy data and requiring minimal training time. The study demonstrates SHRED’s performance using both synthetic CFD data and experimental temperature recordings, achieving low reconstruction errors and showcasing its potential for real‑time monitoring and digital twin development.
By Stefano Riva, Carolina Introini, Jos\`e Nathan Kutz, Antonio Cammi
This paper presents a novel Shallow Recurrent Decoder network for efficient parametric state estimation in circulating fuel reactors, specifically applied to the Molten Salt Fast Reactor (MSFR). The model infers the full reactor state—including neutron fluxes, precursor concentrations, temperature, pressure, and velocity—using only three out‑of‑core neutron flux time‑series measurements, while also handling parametric time‑series data to explore different accident scenarios. The approach demonstrates accurate real‑time reconstruction with low training cost and provides uncertainty quantification, making it suitable for monitoring and control within a reactor digital twin.
By Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi
arXiv:2607. 18164v1 Announce Type: cross Abstract: Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift.
By Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria, Seul Lee, Daniel Apley, Wei Chen
The paper presents a hybrid edge‑cloud digital twin for welfare‑constrained environmental control in poultry farms. It combines distributed sensing, on‑device state estimation, a physics‑based thermodynamic model with a learned residual, and model predictive control to adaptively manage temperature and ammonia levels. Experiments in a broiler testbed show significant improvements: temperature prediction error drops from 1.8 °C to 0.4 °C, ammonia violations fall by 90 %, and communication needs are reduced 30‑fold, with a Domain Transfer Score of 0.92 indicating strong cross‑facility robustness.
By Suresh Neethirajan
arXiv:2608. 16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS).
By Konstantinos E. Kampourakis, Vasileios Gkioulos, Sokratis Katsikas
arXiv:2609.40071v1 Announce Type: cross
Abstract: Digital twins increasingly support downstream analytical tasks that depend on time-series data, motivating interest in time-series foundation models...
By Sizhe Ma, Katherine A. Flanigan, Mario Berg\'es
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
The paper introduces a closed‑loop cyber‑physical system for autonomous model lifecycle management in automotive manufacturing, deployed since 2023. It manages paired physics and reinforcement‑learning models, selecting the best candidate through competitive retraining cycles and a Conductor orchestrator that handles plant‑wide inventories and fallback controls. The system incorporates an operator‑trust gate that rejects 23% of policies that deviate from established practice, achieving 28‑45% process stability improvements with no safety incidents.
By Zhengyang (Cissy), Gu, Thomas Cook, Fredaljohn Rohrbaugh, Joseph E. Hernandez, Chris Couch
arXiv:2609.17101v1 Announce Type: new
Abstract: The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e....
By Diana A. Bistrian
The paper introduces a four‑layer architecture for Cognitive Digital Twins (CDTs) that extends traditional Digital Twin systems by embedding cognitive capabilities. It outlines a self‑evolving closed loop across physical, digital‑twin, cognitive, and task layers, enabling task‑specific cognitive models and decision generation that are refined through operational feedback. Two operation modes—user‑request‑driven and self‑driven cognition—are described, along with key enabling mechanisms and a lightweight simulation demonstrating closed‑loop feasibility and efficiency gains.
By Haoran Gao, An Li, Zhen Li, Jun Cai
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:2606. 07550v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly and risky.
By Yang Fu, Haomin Bao, Rohit Sonker, Xiaoyan Hu, Aravind Venugopal, Jeff Schneider, Jiayu Chen