INI-VPINN is a new weak-form PINN formulation that implicitly enforces Neumann and interface conditions for Poisson and Laplace problems in multi-material domains with geometric singularities.
Extreme Mechanics Letter63(2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The authors present a Python library and discrete variational framework for training neural networks to solve PDEs like Stokes equations with a robust loss function tied to the true discrete error.
citing papers explorer
-
INI-VPINN: A Variational Physics-Informed Neural Network with Implicit Neumann and Interface Handling for Multi-Material Domains with Geometric Singularities
INI-VPINN is a new weak-form PINN formulation that implicitly enforces Neumann and interface conditions for Poisson and Laplace problems in multi-material domains with geometric singularities.
-
Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
The authors present a Python library and discrete variational framework for training neural networks to solve PDEs like Stokes equations with a robust loss function tied to the true discrete error.