This paper presents the fundamentals of Physics Informed Neural Networks (PINNs) and
reviews literature on the methodology and application of PINNs. PINNs are universal …

This study explores the potential of physics-informed neural networks (PINNs) for the
realization of digital twins (DT) from various perspectives. First, various adaptive sampling …

While a big wave of artificial intelligence (AI) has propagated to the field of computational
fluid dynamics (CFD) acceleration studies, recent research has highlighted that the …

In this paper, a physics-informed neural network (PINN) technique is developed to study a
two-phase film boiling heat transfer process. Data generated through computational fluid …

Hybrid modeling combines physical and data-driven models to improve the performance of
industrial soft sensors. However, simplified physical assumptions and extensive parameter …

This study aims to achieve real-time estimation of the full-field strain distribution in a
structure by signals measured from several strain gauges attached to the structure. Our …

In this study, we propose a method to infer the displacement and stress of the entire domain
using physics-informed neural networks (PINNs), utilizing locally measured strain data from …

The heating process in hot-rolled steel manufacture is a key step for product quality. It is
desired that the steel slabs can be heated to the target temperature in the furnace with good …

Ensuring precise prediction, monitoring, and control of frictional contact temperature is
imperative for the design and operation of advanced equipment. Currently, the …

Many gas turbine and rocket engines exhibit unwanted combustion instability at the
experimental testing phase. Instability leads to large amplitude pressure oscillations and …