Physics-informed neural networks (PINNs) represent an emerging computational paradigm
that incorporates observed data patterns and the fundamental physical laws of a given …

In this review, state‐of‐the‐art studies on the uncertainty quantification (UQ) of
microstructures in aerospace materials is examined, addressing both forward and inverse …

Classical numerical methods such as finite element method (FEM) face limitations due to
their low efficiency when addressing large-scale problems. As a novel paradigm, the physics …

The rapid development of deep learning has significant implications for the advancement of
computational fluid dynamics. Currently, most pixel-grid-based deep learning methods for …

It is difficult to accurately predict the flow field over an aircraft in the presence of shock waves
due to its strong nonlinear characteristics. In this study, we developed an accuracy …

Efficient and accurate prediction of the wind turbine dynamic wake is crucial for active wake
control and load assessment in wind farms. This paper proposes a real-time dynamic wake …

The layout of battery cells in liquid-based battery thermal management systems determines
the temperature distribution within a battery pack, which, in turn, affects the safety, reliability …

We develop a numerical method for fast computation of natural convection, which proposes
a new dimensionless number (Fs) to characterize the degree of influence of convection on …

This study presents a novel strategy for constructing an approximator for arbitrary univariate
functions. The proposed approximation utilizes the anti-derivatives of a Fourier series …

This work proposes a novel surrogate model (noted as HCP-PIGN) combining two groups of
neural networks: ie, the physics-informed and the graph convolutional neural networks …