Results 41 to 50 of about 16,348,860 (150)
Numerical MLPG Analysis of Piezoelectric Sensor in Structures
Slovak Journal of Civil Engineering, 2014 The paper deals with a numerical analysis of the electro-mechanical response of piezoelectric sensors subjected to an external non-uniform displacement field.
Staňák Peter +3 more
doaj +1 more source
Meshless Local Petrov-Galerkin (MLPG) Method for Convection-Diffusion Problems [PDF]
, 2000 Due to the very general nature of the Meshless Local Petrov-Galerkin (MLPG) method, it is very easy and natural to introduce the upwinding concept (even in multi-dimensional cases) in the MLPG method, in order to deal with convection-dominated flows.
H. Lin, S.N. Atluri
core +1 more source
Exact Dirichlet boundary multi‐resolution hash encoding solver for structures
Computer-Aided Civil and Infrastructure Engineering, Volume 40, Issue 25, Page 4172-4192, 20 October 2025.Abstract Designed to address computationally expensive scientific problems, physics‐informed neural networks (PINNs) have primarily focused on solving issues involving relatively simple geometric shapes. Drawing inspiration from exact Dirichlet boundary PINN and neural representation field, this study first develops a multi‐resolution hash encoding ...
Xiaoge Tian, Jiaji Wang, Xinzheng Lu
wiley +1 more source
A class of Petrov-Galerkin finite element methods for the numerical solution of the stationary convection-diffusion equation. [PDF]
, 1996 A class of Petrov-Galerkin finite element methods is proposed for the numerical solution of the n dimensional stationary convection-diffusion equation.
Perella, A.J., Perella, Andrew James
core
International Journal of Mechanical System Dynamics, Volume 5, Issue 1, Page 40-66, March 2025.ABSTRACT This research article introduces a high‐order finite element model based on the first‐order shear deformation theory to analyze the hygrothermal static responses of nanoscale, multidirectional nanofunctionally graded piezoelectric (NFGP) plates resting on variable elastic foundations. The study considers the material properties of these plates,
Pawan Kumar, Suraj Prakash Harsha
wiley +1 more source
Slovak Journal of Civil Engineering, 2014 A numerical analysis based on the meshless local Petrov- Galerkin (MLPG) method is proposed for a functionally graded material FGM (FGMfunctionally graded material) beam.
Sátor Ladislav +2 more
doaj +1 more source
International Journal of Mathematics and Mathematical Sciences, Volume 2025, Issue 1, 2025.This paper introduces a coupled physics‐informed neural network (C–PINN) framework for simulating electron–lattice thermal conduction. The framework uses an additive Δ‐decomposition to couple the electron temperature Te and lattice temperature Tl, with a learnable correction term Δ(t, x) capturing spatial–temporal deviations from thermal equilibrium ...
Zhihong Che +5 more
wiley +1 more source
International Journal of RF and Microwave Computer-Aided Engineering, Volume 2025, Issue 1, 2025.The design and analysis of nanoelectromechanical systems (NEMSs) provide significant challenges due to the dominance of surface forces, quantum‐scale effects, and complex material properties, where classical electrostatics and continuum mechanics become inadequate.
N. M. Mary Sindhuja +4 more
wiley +1 more source
Node-to-Node Realization of Meshless Local Petrov Galerkin (MLPG) Fully in GPU
IEEE Access, 2019 This paper presents an end-to-end massively parallelized procedure for the solution of boundary value problems on Graphics Processing Units (GPU). The proposal is an integrated strategy that not only entails the calculation of nodal contributions, and ...
Lucas Pantuza Amorim +3 more
doaj +1 more source
Physics‐informed neural operator solver and super‐resolution for solid mechanics
Computer-Aided Civil and Infrastructure Engineering, Volume 39, Issue 22, Page 3435-3451, 15 November 2024.Abstract Physics‐Informed Neural Networks (PINNs) have solved numerous mechanics problems by training to minimize the loss functions of governing partial differential equations (PDEs). Despite successful development of PINNs in various systems, computational efficiency and fidelity prediction have remained profound challenges.
Chawit Kaewnuratchadasorn +2 more
wiley +1 more source