Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations
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 …
equations of Boussinesq type, known as the Serre–Green–Naghdi system; it describes …
Learning to optimize multigrid PDE solvers
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 …
many scientific disciplines. A leading technique for solving large-scale PDEs is using …
Massive computational acceleration by using neural networks to emulate mechanism-based biological models
For many biological applications, exploration of the massive parametric space of a
mechanism-based model can impose a prohibitive computational demand. To overcome …
mechanism-based model can impose a prohibitive computational demand. To overcome …
A gradient-enhanced physics-informed neural network (gPINN) scheme for the coupled non-fickian/non-fourierian diffusion-thermoelasticity analysis: A novel gPINN …
K Eshkofti, SM Hosseini - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
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 …
enhanced physics-informed neural network (gPINN) with a novel structure for the …
A deep learning feed-forward neural network framework for the solutions to singularly perturbed delay differential equations
SM Mallikarjunaiah - Applied Soft Computing, 2023 - Elsevier
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 …
framework as a numerical tool for approximating the solutions to singularly perturbed delay …
Highly efficient modeling and optimization of neural fiber responses to electrical stimulation
Peripheral neuromodulation has emerged as a powerful modality for controlling
physiological functions and treating a variety of medical conditions including chronic pain …
physiological functions and treating a variety of medical conditions including chronic pain …
Deep learning of turbulent scalar mixing
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 …
models, we put forth a framework for discovering turbulence models from scattered and …
Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian mesh
The Poisson equation is commonly encountered in engineering, for instance, in
computational fluid dynamics (CFD) where it is needed to compute corrections to the …
computational fluid dynamics (CFD) where it is needed to compute corrections to the …
Explainable artificial intelligence for mechanics: physics-explaining neural networks for constitutive models
(Artificial) neural networks have become increasingly popular in mechanics and materials
sciences to accelerate computations with model order reduction techniques and as …
sciences to accelerate computations with model order reduction techniques and as …
Sensitivity analysis and performance evaluation of the PEMFC using wave-like porous ribs
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 …
performance at low current densities is to enhance the convective heat transfer in the gas …