Variational PINNs with tree-based integration and boundary element data in the modeling of multi-phase architected materials
Dimitrios C. Rodopoulos, Panos Pantidis, Nikolaos Karathanasopoulos

TL;DR
This paper introduces a novel VPINN framework utilizing tree-based integration and boundary element data for accurate and efficient modeling of complex multiphase architected materials, improving upon classical PINNs.
Contribution
The paper develops a variational PINN approach with boundary element data and adaptive integration, enhancing modeling accuracy and computational efficiency for multiphase materials.
Findings
VPINNs accurately capture deformation fields in multiphase architectures.
Tree-based integration reduces computational cost while handling material discontinuities.
Incorporating semi-analytical info improves model performance.
Abstract
The current contribution develops a Variational Physics-Informed Neural Network (VPINN)-based framework for the analysis and design of multiphase architected solids. The elaborated VPINN methodology is based on the Petrov-Galerkin approach, with a deep neural network acting as trial function and local polynomials as test functions. For the analysis, a Galerkin Boundary Element Method (GBEM) scheme is developed to generate the mechanical field data, employing solely domain boundary information. The VPINN methodology is complemented by an adaptive, tree-based integration scheme for the evaluation of the weak-form integrals. Different double-phase material architectures are considered, with the VPINNs demonstrating their ability to capture the deformation fields with considerable accuracy. Moreover, the performance enhancement by the incorporation of additional semi-analytical information…
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