We present the first hard-constraint neural network model for predicting activity coefficients
(HANNA), a thermodynamic mixture property that is the basis for many applications in …

In this study, a physics-informed neural network (PINN)-based surrogate model was
proposed for a virtual thermal sensor (VTS) with real-time simulation. This surrogate model …

Abstract Machine learning methods have recently shown a broad application prospect in
biomass gasification modeling. However, a significant drawback of the machine learning …

This paper presents a predictive computational framework for surrogate modeling of
pressure field and optimization of pressure sensor placement for wind engineering …

Selective laser melting (SLM) additive manufacturing (AM) is widely used due to its
significant advantages in designing and manufacturing special-shaped complex …

Accurate force–deformation hysteretic models for structures, components, and materials are
essential for structural analysis. The development of an explicit mathematical model for …

This paper proposes an improved version of physics-informed neural networks (PINNs), the
physics-informed kernel function neural networks (PIKFNNs), to solve various linear and …

Abstract Machine learning (ML) has been widely used to design and develop new materials
owing to its low computational cost and powerful predictive capabilities. In recent years, the …

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 …

Steels are widely used as structural materials, making them essential for supporting our lives
and industries. However, further improving the comprehensive properties of steel through …