Neural network-based approaches have emerged as alternatives to conventional
computational fluid dynamics (CFD) in solving multiphase flow problems. However, most of …

Computational fluid dynamics (CFD) can be a powerful tool for higher-fidelity water
infrastructure planning and design. Despite decades of development and demonstration …

To alleviate the problem of high-fidelity data dependence and inexplicability in pure data-
driven neural network models, physical informed neural networks (PINNs) provide a new …

The efficient and precise reconstruction of supersonic combustion flow fields enables real-
time sensing and control of hypersonic vehicles. However, current flow field reconstruction …

Physics-informed neural networks (PINNs) have emerged as a promising alternative to
conventional computational fluid dynamics (CFD) approaches for solving and modeling …

In recent years, physics-informed neural networks (PINNs) have shown potential as a
method for solving combustion physics. However, current efforts using PINNs for the direct …

Physics informed neural networks (PINNs) have effectively demonstrated the ability to
approximate the solutions of a system of partial differential equations (PDEs) by embedding …

In engineering applications, deflectors play a vital role in regulating the uniformity of flow
field distribution in the selective catalytic reduction (SCR) system, and their optimal design is …

Abstract Machine learning (ML) is increasingly implemented to model water infrastructure
dynamics. Common ML models are primarily data-driven and require a significant amount of …

High Mach number inlets face complex challenges such as shock wave/boundary layer
interference, adversely impacting the aerodynamic characteristics and stable operating …