Data-enabled physics-informed machine learning for reduced-order modeling digital twin: application to nuclear reactor physics

H Gong, S Cheng, Z Chen, Q Li - Nuclear Science and Engineering, 2022 - Taylor & Francis
This paper proposes an approach that combines reduced-order models with machine
learning in order to create physics-informed digital twins to predict high-dimensional output …

An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

H Gong, S Cheng, Z Chen, Q Li… - Annals of nuclear …, 2022 - Elsevier
This paper proposes an approach that combines reduced-order models with machine
learning in order to create an digital twin to predict the power distribution over the core …

Stability of discrete empirical interpolation and gappy proper orthogonal decomposition with randomized and deterministic sampling points

B Peherstorfer, Z Drmac, S Gugercin - SIAM Journal on Scientific Computing, 2020 - SIAM
This work investigates the stability of (discrete) empirical interpolation for nonlinear model
reduction and state field approximation from measurements. Empirical interpolation derives …

Stabilization of Generalized Empirical Interpolation Method (GEIM) in presence of noise: A novel approach based on Tikhonov regularization

C Introini, S Cavalleri, S Lorenzi, S Riva… - Computer Methods in …, 2023 - Elsevier
Abstract The Empirical Interpolation Method (EIM), and its generalized version (GEIM), are
non-intrusive, reduced-basis model order reduction methods hereby adopted and modified …

Parameter identification and state estimation for nuclear reactor operation digital twin

H Gong, T Zhu, Z Chen, Y Wan, Q Li - Annals of Nuclear Energy, 2023 - Elsevier
Abstract Reactor Operation Digital Twin (RODT) is now receiving increasing attention and
investment in nuclear engineering domain. A prototype of a RODT was first brought out by …

Generalized Empirical Interpolation Method With H 1 Regularization: Application to Nuclear Reactor Physics

H Gong, Z Chen, Q Li - Frontiers in Energy Research, 2022 - frontiersin.org
The generalized empirical interpolation method (GEIM) can be used to estimate the physical
field by combining observation data acquired from the physical system itself and a reduced …

[HTML][HTML] Multi-physics model bias correction with data-driven reduced order techniques: Application to nuclear case studies

S Riva, C Introini, A Cammi - Applied Mathematical Modelling, 2024 - Elsevier
Due to the multiple physics involved and their mutual and complex interactions, nuclear
engineers and researchers are constantly working on developing highly accurate Multi …

[HTML][HTML] Systematic sensor placement for structural anomaly detection in the absence of damaged states

C Bigoni, Z Zhang, JS Hesthaven - Computer Methods in Applied …, 2020 - Elsevier
In structural health monitoring (SHM), risk assessment and decision strategies rely primarily
on sensor responses. Simulated data can be generated to emulate the monitoring …

Observation data compression for variational assimilation of dynamical systems

S Cheng, D Lucor, JP Argaud - Journal of computational science, 2021 - Elsevier
Accurate estimation of error covariances (both background and observation) is crucial for
efficient observation compression approaches in data assimilation of large-scale dynamical …

A POD reduced-order model for resolving the neutron transport problems of nuclear reactor

Y Sun, J Yang, Y Wang, Z Li, Y Ma - Annals of Nuclear Energy, 2020 - Elsevier
Due to the high-dimensional integro-differential properties of the neutron transport equation
(NTE) and the large-scale property of the nuclear reactor system, the detailed neutron …