Natural gas pipeline systems are commonly designed under the assumption of constant
supply and demand flow conditions. This is while gas flows are transient because of the …

This paper proposes a modified artificial intelligence (AI) approach based on the gradient-
enhanced physics-informed neural network (gPINN) with a novel structure for the …

Prediction and uncertainty quantification of tumor progression are vital in clinical practice, ie,
disease prognosis and decision-making on treatment strategies. In this work, we propose …

In many cutting-edge applications, high-fidelity computational models prove to be too slow
for practical use and are therefore replaced by much faster surrogate models. Recently …

The heat transfer through a concave permeable fin is analyzed by the local thermal non-
equilibrium (LTNE) model. The governing dimensional temperature equations for the solid …

Efficient and accurate prediction of the flow field in turbomachinery is vital for tasks such as
optimization and off-design modeling. Deep learning methods offer inspiring tools for flow …

Sea surface temperature (SST) prediction has attracted increasing attention, due to its
crucial role in understanding the Earth's climate and ocean system. Existing SST prediction …

Background: A generalized model of mathematical nature is considered to address the
viscoelastic flow problem using fractional derivatives. Control/freedom of the flow …

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 …

We propose TransFlowNet, a novel physics-constrained deep learning framework that
focuses on the spatio-temporal super-resolution (STSR) of flow simulations. A key insight is …