Isogeometric Convolution Hierarchical Deep-learning Neural Network: Isogeometric analysis with versatile adaptivity

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https://www.sciencedirect.com/science/article/pii/S0045782523004802

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https://doi.org/10.1016/j.cma.2023.116356
 http://hdl.handle.net/11603/30067

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UMBC Mechanical Engineering Department
UMBC Faculty Collection

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https://orcid.org/0000-0003-3698-5596

Date

2023-09-02

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journal articles
postprints
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Citation of Original Publication

Zhang, Lei, Chanwook Park, Ye Lu, Hengyang Li, Satyajit Mojumder, Sourav Saha, Jiachen Guo, et al. “Isogeometric Convolution Hierarchical Deep-Learning Neural Network: Isogeometric Analysis with Versatile Adaptivity.” Computer Methods in Applied Mechanics and Engineering, September 2, 2023, 116356. https://doi.org/10.1016/j.cma.2023.116356.

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Abstract

We are witnessing a rapid transition from Software 1.0 to 2.0. Software 1.0 focuses on manually designed algorithms, while Software 2.0 leverages data and machine learning algorithms (or artificial intelligence) for optimized, fast, and accurate solutions. For the past few years, we have been developing Convolution Hierarchical Deep-learning Neural Network Artificial Intelligence (C-HiDeNN-AI), which enables the realization of Engineering Software 2.0 by opening the next-generation neural network-based computational tools that can simultaneously train data and solve mechanistic equations. This paper focuses on solving partial differential equations with C-HiDeNN. Still, the same neural network can be used for training and calibration with experimental data, which will be discussed in a separate paper. This paper presents a computational framework combining the C-HiDeNN theory with isogeometric analysis (IGA), called Convolution IGA (C-IGA). C-IGA has five key features that advance IGA: (1) arbitrarily high-order smoothness and convergence rates without increasing degrees of freedom; (2) a Kronecker delta property that enables direct imposition of Dirichlet boundary conditions; (3) automatic and flexible global/local mesh-adaptivity with built-in length scale control and adjustable radial basis functions; (4) ability to handle irregular meshes and triangular/tetrahedral elements; and (5) GPU implementation that speeds up the program as fast as finite element method (FEM). Mathematically, we prove that both IGA and C-IGA mappings are equivalent, and by taking a special design and modified anchors as nodes, C-IGA degenerates to IGA. We demonstrate the accuracy, convergence rates, mesh-adaptivity, and performance of C-IGA with several 1D, 2D, and 3D numerical examples. The future applications of C-IGA from topology optimization to product manufacturing with multi-GPU programming are discussed.