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 work, a least-square finite difference-based physics-informed neural network (LSFD-
PINN) is proposed to simulate steady incompressible flows. The original PINN employs the …

Hybrid fluid–particle systems are prevalent in nature and engineering practices, but
accurately simulating the dynamic behaviour is challenging due to their inherent strong non …

In recent years, the advancement of deep learning has led to the utilization of related
technologies to enhance the efficiency and accuracy of scientific computing. Physics …

Although fuel spray parameters, such as spray cone angle and penetration length, are
crucial for developing high-efficiency and high-performance combustion engines, general …

Understanding fluid-particle interactions is crucial due to their occurrence in both natural
phenomena and engineering applications, but accurately capturing these interactions …

In this work, an immersed boundary method-incorporated physics informed neural network
(IBM-PINN) is proposed to simulate steady incompressible flows around immersed objects …

Recently, physics-informed neural networks (PINNs) have aroused an upsurge in the field of
scientific computing including solving partial differential equations (PDEs), which convert the …

Physics-informed neural networks (PINNs) improve the accuracy and generalization ability
of prediction by introducing physical constraints in the training process. As a model …

Physics-informed neural networks (PINNs) typically involve higher-order partial derivatives
with respect to their inputs, which are too costly to compute and store by using automatic …