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

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 …

We present two effective methods for solving high-dimensional partial differential equations
(PDE) based on randomized neural networks. Motivated by the universal approximation …

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 …

In this paper, we study data-driven localized wave solutions and parameter discovery in the
massive Thirring (MT) model via the deep learning in the framework of physics-informed …

We consider the approximation of a class of dynamic partial differential equations (PDEs) of
second order in time by the physics-informed neural network (PINN) approach, and provide …

We show that the error achievable using physics-informed neural networks for solving
differential equations can be substantially reduced when these networks are trained using …

This paper proposes a novel framework for simulating the dynamics of beams on elastic
foundations. Specifically, partial differential equations modeling Euler–Bernoulli and …

In this paper, we introduce a shallow (one-hidden-layer) physics-informed neural network
(PINN) for solving partial differential equations on static and evolving surfaces. For the static …

Physics-informed machine learning (PIML) as a means of solving partial differential
equations (PDEs) has garnered much attention in the Computational Science and …