Physics-informed neural networks (PINNs) and their variants have been very popular in
recent years as algorithms for the numerical simulation of both forward and inverse …

Having made significant advancements in understanding living organisms at various levels
such as genes, cells, molecules, tissues, and pathways, the field of life sciences is now …

We develop a distributed framework for the physics-informed neural networks (PINNs)
based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs …

Physics-informed neural networks (PINNs) are a powerful approach for solving problems
involving differential equations, yet they often struggle to solve problems with high frequency …

Through the previous three editions, Handbook of Differential Equations has proven an
invaluable reference for anyone working within the field of mathematics, including …

Solving time-harmonic wave propagation problems by iterative methods is a difficult task,
and over the last two decades an important research effort has gone into develo** …

Most modeling approaches lie in either of the two categories: physics‐based or data‐driven.
Recently, a third approach which is a combination of these deterministic and statistical …

With the increase in computational power in the last decade and the forthcoming Exascale
supercomputers, a new horizon in computational modelling and simulation is envisioned in …

This article considers the criteria for the existence of established modes of power systems.
Nonlinear nodal equations of steady-state modes are presented, which have many solutions …

This article deals with microwave tomography for brain stroke imaging using state-of-the-art
numerical modeling and massively parallel computing. Iterative microwave tomographic …