High-dimensional dynamical systems often require computationally intensive physics-based
simulations, making full physical space data assimilation impractical. Latent data …

The fast reconstruction of neutronic field in a nuclear core using reduced modeling and
limited observations has attracted considerable attention. In particular, four design …

Global wildfire models play a crucial role in anticipating and responding to changing wildfire
regimes. JULES-INFERNO is a global vegetation and fire model simulating wildfire …

In this paper, a systematic uncertainty quantification of dynamic mode decomposition (DMD)
for xenon dynamic prediction is brought out based on HPR1000 reactor. The DMD method is …

The rapid growth of data-driven applications is ubiquitous across virtually all scientific
domains, and has led to an increasing demand for effective methods to handle data …

This work proposes two reduced-order methods to address the high computing resource
consumption of neutron transport kinetics problems. The first method is based on proper …

Accurately predicting three-dimensional power distribution within a reactor core during
reactivity transients is crucial for optimizing reactor operational control. This paper proposes …

In this paper, a dynamic prediction scheme that combines the data assimilation method and
dynamic mode decomposition (DMD) is brought out for the prediction of the whole-core …

This study proposes a method based on Dynamic Mode Decomposition (DMD) for predicting
the state transitions of core dynamics and xenon transients in nuclear reactors. Both types of …

Abstract The Dynamic Mode Decomposition (DMD) algorithm can extract the mode of
nuclide densities and effectively reduce the computational burden brought by the burnup …