Singular Layer Pinn Methods for Burgers' Equation
In this article, we present a new learning method called sl-PINN to tackle the one-dimensional viscous Burgers problem at a small viscosity, which results in a singular interior layer. To address this issue, we first determine the corrector that characterizes the unique behavior of the viscous flow within the interior layers by means of asymptotic ...
Teng-Yuan Chang +3 more
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PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks [PDF]
Physics-Informed Neural Networks (PINNs) solve partial differential equations (PDEs) by embedding governing equations and boundary/initial conditions into the loss function. However, enforcing Dirichlet boundary conditions accurately remains challenging,
Sobh, Nahil +2 more
core +1 more source
The folder contains training data from the field trials and results used for ...
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Inversa värmetransportproblem med Physics-Informed Neural Networks (PINN) [PDF]
Den partiella differentialekvationen (PDE) för värmeledning beskriver hur värme sprids genom ett medium. Att lösa det stationära värmeledningsproblemet inverst, det vill säga lösa ut den bakomliggande värmediffusiviteten från temperaturfältet, kan bli ...
Andreasson, Alvin +5 more
core
Causality-enhanced and symmetry-preserving PINNs for damped elastodynamic PDEs. [PDF]
PLoS OneIsrar M, Ahmad S, Zaman FD, Ali I.
europepmc +1 more source
PINN for Nonlocal Beam Eigenvalue Problems
Example code used in: Das, B., Barretta, R., Čanađija, M. (2025) Physics-Informed Neural Networks for Nonlocal Beam Eigenvalue Problems. Training of a PINN for nonlocal cantilever beam.
Canadija, Marko
core +1 more source
Decoupled Spatiotemporal Learning for Multi-Location Beam Response Reconstruction from Sparse Deflection Measurements. [PDF]
Sensors (Basel)Luo Z, Yan F, Wen Y, Geng Y, Yan D.
europepmc +1 more source
CoRoPINN: Cognitive region optimized physics-informed neural networks. [PDF]
PLoS OneLi J, Liao Y, Wang X, Deng J.
europepmc +1 more source
PINN-PBE Model for Describing Gibbsite Crystallization Dynamics
The industrial testing of the optimal control system for the gibbsite precipitation area revealed the cases where the optimizer finds and exploits vulnerabilities in the predictive data-driven model, recommending erroneous control actions.
Nickolai V. Tuleshov +2 more
core +1 more source
AI-driven predictive modelling of residual stress of HVOF thermal sprayed carbon-based composite coatings using physics-informed neural networks. [PDF]
Sci RepTyagi A, Dadhich A, Sirohi S, Gupta AK.
europepmc +1 more source