Charlie C.L. Wang’s Post

Thrilled to collaborate with Prof. Gang Xu on accelerating the computation of isogeometric analysis (IGA) for topology-consistent models - a rewarding partnership that began during his visit to Delft in 2016. In this recent paper accepted by Journal of Computational Physics, we present IGA-Graph-Net, an innovative isogeometric analysis framework that leverages Graph Neural Networks and introduces a custom loss function to address Dirichlet boundary conditions, achieving significantly improved accuracy over traditional CNN-based methods for isogeometric analysis on complex geometries. By integrating ResNetV2 and PointTransformer, the framework effectively handles B-spline data and complex boundary conditions, demonstrating enhanced performance on partial differential equation datasets. "IGA-Graph-Net: Isogeometric analysis-reuse method based on graph neural networks for topology-consistent models", Journal of Computational Physics, 2024 (https://lnkd.in/erS5q6ZF) "IGA-Reuse-NET: A deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization", Computer Aided Geometric Design (https://lnkd.in/e_WiJzFt) "Isogeometric computation reuse method for complex objects with topology-consistent volumetric parameterization", Computer-Aided Design (https://lnkd.in/eZKDsip4) #isogeometricanalysis #machinelearning