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  1. No Access

    Article

    Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C-HiDeNN-TD) for high-resolution topology optimization

    High-resolution structural topology optimization is extremely challenging due to a large number of degrees of freedom (DoFs). In this work, a Convolution-Hierarchical Deep Learning Neural Network-Tensor Decomp...

    Hengyang Li, Stefan Knapik, Yangfan Li, Chanwook Park… in Computational Mechanics (2023)

  2. No Access

    Article

    Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration

    We propose the Convolution Hierarchical Deep-learning Neural Network (C-HiDeNN) that can be tuned to have superior accuracy, higher smoothness, and faster convergence rates like higher order finite element met...

    Chanwook Park, Ye Lu, Sourav Saha, Tianju Xue, Jiachen Guo… in Computational Mechanics (2023)

  3. No Access

    Article

    Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond

    This paper presents a general Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN) computational framework for solving partial differential equations. This is the first paper of a series of papers...

    Ye Lu, Hengyang Li, Lei Zhang, Chanwook Park, Satyajit Mojumder… in Computational Mechanics (2023)

  4. No Access

    Article

    Deep Learning Discrete Calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research

    The article proposes formulating and codifying a set of applied numerical methods, coined as Deep Learning Discrete Calculus (DLDC), that uses the knowledge from discrete numerical methods to interpret the deep l...

    Sourav Saha, Chanwook Park, Stefan Knapik, Jiachen Guo… in Computational Mechanics (2023)

  5. No Access

    Article

    HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis

    The hierarchical deep-learning neural network (HiDeNN) (Zhang et al. Computational Mechanics, 67:207–230) provides a systematic approach to constructing numerical approximations that can be incorporated into a...

    Yingjian Liu, Chanwook Park, Ye Lu, Satyajit Mojumder… in Computational Mechanics (2023)

  6. No Access

    Article

    Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

    Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient’s initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework...

    Mahsa Tajdari, Farzam Tajdari, Pouyan Shirzadian… in Engineering with Computers (2022)

  7. No Access

    Article

    A Nano-Micro–Macro Multiscale Modeling for Carbon Fiber-Reinforced Graphene/Epoxy Nanocomposites

    A new nano-micro–macro multiscale modeling approach that combines molecular dynamic (MD) simulations with micromechanics and stochastic continuum models is proposed to model carbon-fiber-reinforced graphene/ep...

    Ho-il Choi, Chanwook Park, Hyoung Jun Lim… in Multiscale Science and Engineering (2021)