当前位置: X-MOL 学术Comput. Struct. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Finite element network analysis: A machine learning based computational framework for the simulation of physical systems
Computers & Structures ( IF 4.7 ) Pub Date : 2021-02-22 , DOI: 10.1016/j.compstruc.2021.106484
Mehdi Jokar , Fabio Semperlotti

This paper introduces the concept of finite element network analysis (FENA) which is a physics-informed, machine-learning-based, computational framework for the simulation of physical systems. The framework leverages the extreme computational speed of trained neural networks and the unique transfer knowledge property of bidirectional recurrent neural networks (BRNN) to provide a uniquely powerful and flexible computing platform. One of the most remarkable properties of this framework consists in its ability to simulate the response of physical systems, made of multiple interconnected components, by combining individually pre-trained network models that do not require any further training following the assembly phase. This remarkable result is achieved via the use of key concepts such as transfer knowledge and network concatenation. Although the computational framework is illustrated and numerically validated for the case of a 1D elastic bar under static loading, the conceptual structure of the framework is extremely general and it suggests potential extensions to a broad spectrum of applications in computational science. The framework is numerically validated against the solution provided by traditional finite element analysis and the results highlight the outstanding performance of this new concept of computational platform.



中文翻译:

有限元网络分析:一种基于机器学习的计算框架,用于物理系统的仿真

本文介绍了有限元网络分析(FENA)的概念,该概念是基于物理的,基于机器学习的,用于物理系统仿真的计算框架。该框架利用了受过训练的神经网络的极高计算速度和双向递归神经网络(BRNN)的独特转移知识特性,从而提供了独特而强大且灵活的计算平台。该框架最显着的特性之一在于它具有以下能力:通过组合单独的预训练的网络模型来模拟由多个相互连接的组件组成的物理系统的响应,这些模型不需要在组装阶段进行任何进一步的训练。通过使用诸如传输知识和网络连接之类的关键概念,可以实现这一非凡的结果。尽管对一维弹性杆在静态载荷情况下的计算框架进行了说明并进行了数值验证,但该框架的概念结构极为笼统,它暗示了对计算科学中广泛应用的潜在扩展。该框架针对传统有限元分析提供的解决方案进行了数值验证,结果突出了这种新概念的计算平台的出色性能。

更新日期:2021-02-23
down
wechat
bug