We consider strongly-nonlinear and weakly-dispersive surface water waves governed by
equations of Boussinesq type, known as the Serre–Green–Naghdi system; it describes …

Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for
many scientific disciplines. A leading technique for solving large-scale PDEs is using …

For many biological applications, exploration of the massive parametric space of a
mechanism-based model can impose a prohibitive computational demand. To overcome …

This paper proposes a modified artificial intelligence (AI) approach based on the gradient-
enhanced physics-informed neural network (gPINN) with a novel structure for the …

In this paper, we explore a deep learning feedforward artificial neural network (ANN)
framework as a numerical tool for approximating the solutions to singularly perturbed delay …

Peripheral neuromodulation has emerged as a powerful modality for controlling
physiological functions and treating a variety of medical conditions including chronic pain …

Based on recent developments in physics-informed deep learning and deep hidden physics
models, we put forth a framework for discovering turbulence models from scattered and …

The Poisson equation is commonly encountered in engineering, for instance, in
computational fluid dynamics (CFD) where it is needed to compute corrections to the …

(Artificial) neural networks have become increasingly popular in mechanics and materials
sciences to accelerate computations with model order reduction techniques and as …

An approachable way to improve proton exchange membrane fuel cell's (PEMFC)
performance at low current densities is to enhance the convective heat transfer in the gas …