MBD-NODE:受约束多体系统的物理信息数据驱动建模与仿真

IF 2.6 2区 工程技术 Q2 MECHANICS
Jingquan Wang, Shu Wang, Huzaifa Mustafa Unjhawala, Jinlong Wu, Dan Negrut
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引用次数: 0

摘要

我们描述了一个框架,该框架可以整合先验物理信息,例如运动学约束条件的存在,以支持多体动力学中的数据驱动仿真。与其他直接模拟系统状态的方法(如基于全连接神经网络(FCNN)或循环神经网络(RNN)的方法)不同,我们提出的方法采用神经常微分方程(NODE)范式,对系统状态的导数进行建模。所提方法的核心部分是其从先前的物理知识和约束条件以及数据输入中学习多体系统动力学的能力。这一学习过程通过约束优化方法得以实现,从而确保在仿真过程中考虑到物理规律和系统约束条件。这项工作的模型、数据和代码可在 https://github.com/uwsbel/sbel-reproducibility/tree/master/2024/MNODE-code 上以开源方式公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MBD-NODE: physics-informed data-driven modeling and simulation of constrained multibody systems

MBD-NODE: physics-informed data-driven modeling and simulation of constrained multibody systems

We describe a framework that can integrate prior physical information, e.g., the presence of kinematic constraints, to support data-driven simulation in multibody dynamics. Unlike other approaches, e.g., Fully Connected Neural Network (FCNN) or Recurrent Neural Network (RNN)-based methods, which are used to model the system states directly, the proposed approach embraces a Neural Ordinary Differential Equation (NODE) paradigm, which models the derivatives of the system states. A central part of the proposed methodology is its capacity to learn the multibody system dynamics from prior physical knowledge and constraints combined with data inputs. This learning process is facilitated by a constrained optimization approach, which ensures that physical laws and system constraints are accounted for in the simulation process. The models, data, and code for this work are publicly available as open source at https://github.com/uwsbel/sbel-reproducibility/tree/master/2024/MNODE-code.

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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
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