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arXiv Machine Learning

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor

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.

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arXiv Machine Learning
Sep 7

Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks

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
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arXiv Machine Learning
Sep 1

The PUR-1 Cyber-Physical Digital Twin

arXiv:2608.30186v1 Announce Type: cross Abstract: Digital twin technologies have the potential to improve operational flexibility and responsiveness capabilities of nuclear systems. To provide decisi...

By Vasileios Theos, Jonah Lau, Konstantinos Gkouliaras, Zachery Dahm, Konstantinos Vasili, Noah Fillgrove, William Richards, True Miller, Brian Jowers, Stylianos Chatzidakis
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arXiv Machine Learning
Aug 11

A Coupled Physics-Informed Neural Network for Greenhouse Climate State Reconstruction and Parameter Identification under Sparse Sensor Measurements

arXiv:2605. 02524v2 Announce Type: replace Abstract: Accurate reconstruction of greenhouse climate variables from sparse sensor measurements is essential for intelligent environmental monitoring, automated climate control, and precision agriculture.

By Sani Biswas, Khursheed J. Ansari, Md. Nasim Akhtar
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arXiv Machine Learning
Sep 4

Observation-Aligned Two-Stage Domain Decomposition for Physics-Informed Traffic State Estimation with Sparse Fixed Sensors

The paper introduces Observation‑Aligned Two‑Stage Domain Decomposition Physics‑Informed Neural Networks (TSDD‑PINN) for reconstructing traffic speed fields from sparse fixed sensors. It first trains a global PINN, then uses its residuals to partition the domain and warm‑start child networks, allowing spatial, temporal, or space‑time refinement. Experiments on the I‑24 MOTION dataset show that TSDD‑PINN achieves lower relative L2 error in most configurations and trains faster than the XPINN baseline, with performance depending on sensing density.

By Eunhan Ka, Ludovic Leclercq, Satish V. Ukkusuri
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