This research aims to study and assess state-of-the-art physics-informed neural networks
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …

In recent years, a plethora of methods combining neural networks and partial differential
equations have been developed. A widely known example are physics-informed neural …

We present a novel, accurate, fast, and robust physics-informed neural network method for
solving problems involving differential equations (DEs), called Extreme Theory of Functional …

Physics-informed neural networks (PINNs) have enabled significant improvements in
modelling physical processes described by partial differential equations (PDEs) and are in …

In this paper, we propose a physics-informed neural network-based scheme to solve time-
varying impulsive fractional differential equations without any labeled data. At first, the …

This work presents a recently developed approach based on physics-informed neural
networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical …

This work aims at accurately solve a thermal creep flow in a plane channel problem, as a
class of rarefied-gas dynamics problems, using Physics-Informed Neural Networks (PINNs) …

Discovering mathematical equations that govern physical and biological systems from
observed data is a fundamental challenge in scientific research. We present a new physics …

This paper presents a novel framework, combining the indirect method and Physics-
Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal …

We use physics-informed neural networks (PINNs) to numerically solve the discrete-time
nonlinear observer-based state estimation problem. Integrated within a single-step exact …