cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics-Reference-Cited by-同舟云学术

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cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics

Published:2024-01 Issue: Volume:496 Page:112578
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Lee Hyeonbeen,Han Seongji,Choi Hee-Sun,Kim Jin-Gyun^ORCID

Funder

Korea Atomic Energy Research Institute

National Research Foundation of Korea

Korea Institute of Energy Technology Evaluation and Planning

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference46 articles.

1. Data-driven simulation for general-purpose multibody dynamics using deep neural networks;Choi;Multibody Syst. Dyn.,2021

2. A DNN-based data-driven modeling employing coarse sample data for real-time flexible multibody dynamics simulations;Han;Comput. Methods Appl. Mech. Eng.,2021

3. An efficient fixed-time increment-based data-driven simulation for general multibody dynamics using deep neural networks;Go;Eng. Comput.,2023

4. Deep learning in fluid dynamics;Kutz;J. Fluid Mech.,2017

5. CFD and artificial neural network modeling of two-phase flow pressure drop;Alizadehdakhel;Int. Commun. Heat Mass Transf.,2009

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hierarchical deep learning-based adaptive time stepping scheme for multiscale simulations;Engineering Applications of Artificial Intelligence;2024-07

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