Kolmogorov-Arnold networks (KANs) have attracted attention recently as an alternative to
multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be …

The use of deep learning methods in scientific computing represents a potential paradigm
shift in engineering problem solving. One of the most prominent developments is Physics …

Despite the success of physics-informed neural networks (PINNs) in approximating partial
differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution in …

Material identification is critical for understanding the relationship between mechanical
properties and the associated mechanical functions. However, material identification is a …

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 …

Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints,
train with a composite loss function that contains multiple training point types: different types …

Physics-informed neural networks and operator networks have shown promise for effectively
solving equations modeling physical systems. However, these networks can be difficult or …

Multiscale problems are challenging for neural network-based discretizations of differential
equations, such as physics-informed neural networks (PINNs). This can be (partly) attributed …

Physics-Informed Neural Networks (PINNs) have emerged as a promising method for
solving partial differential equations (PDEs) in scientific computing. While PINNs typically …

Generalization is a key property of machine learning models to perform accurately on
unseen data. Conversely, in the field of scientific machine learning (SciML), generalization …