IGA-Reuse-NET
A deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization[Formula presented]
Dandan Wang (Hangzhou Dianzi University)
Jinlan Xu (Hangzhou Dianzi University)
Fei Gao (Hangzhou Dianzi University)
C.C. Wang (The University of Manchester, TU Delft - Materials and Manufacturing)
Renshu Gu (Hangzhou Dianzi University)
Fei Lin (Hangzhou Dianzi University)
Timon Rabczuk (Bauhaus University Weimar)
Gang Xu (Hangzhou Dianzi University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
In this paper, a deep learning framework combined with isogeometric analysis (IGA for short) called IGA-Reuse-Net is proposed for efficient reuse of numerical simulation on a set of topology-consistent models. Compared with previous data-driven numerical simulation methods only for simple computational domains, our method can predict high-accuracy PDE solutions over topology-consistent geometries with complex boundaries. UNet3+ architecture with interlaced sparse self-attention (ISSA) module is used to enhance the performance of the network. In addition, we propose a new loss function that combines a coefficients loss and a numerical solution loss. Several training datasets with topology-consistent models are constructed for the proposed framework. To verify the effectiveness of our approach, two different types of Poisson equations with different source functions are solved on three datasets with different topologies. Our framework can achieve a good trade-off between accuracy and efficiency. It outperforms the physics-informed neural network (PINN for short) model and yields promising results of prediction.