Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Reduced-order modelling and low-dimensional surrogate models generated using machine
learning algorithms have been widely applied in high-dimensional dynamical systems to …

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 …

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

Parameter identification for wildfire forecasting models often relies on case-by-case tuning
or posterior diagnosis/analysis, which can be computationally expensive due to the …

This paper presents a new nonlinear non‐intrusive reduced‐order model (NL‐NIROM) that
outperforms traditional proper orthogonal decomposition (POD)‐based reduced order model …

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to
many engineering applications (eg, the process of CO 2 sequestration). Here, we extend …

This paper presents the first classical Convolutional Neural Network (CNN) that can be
applied directly to data from unstructured finite element meshes or control volume grids …

Abstract Machine learning-based modeling of reactor physics problems has attracted
increasing interest in recent years. Despite some progress in one-dimensional problems …

The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the
globe infecting over 150 million people and causing the death of over 3.2 million people …