MBD-NODE: Physics-informed data-driven modeling and simulation of constrained multibody systems
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910523215314944 |
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| author | Wang, Jingquan Wang, Shu Unjhawala, Huzaifa Mustafa Wu, Jinlong Negrut, Dan |
| author_facet | Wang, Jingquan Wang, Shu Unjhawala, Huzaifa Mustafa Wu, Jinlong Negrut, Dan |
| contents | We describe a framework that can integrate prior physical information, e.g., the presence of kinematic constraints, to support data-driven simulation in multi-body dynamics. Unlike other approaches, e.g., Fully-connected Neural Network (FCNN) or Recurrent Neural Network (RNN)-based methods that are used to model the system states directly, the proposed approach embraces a Neural Ordinary Differential Equation (NODE) paradigm that 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08664 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MBD-NODE: Physics-informed data-driven modeling and simulation of constrained multibody systems Wang, Jingquan Wang, Shu Unjhawala, Huzaifa Mustafa Wu, Jinlong Negrut, Dan Computational Engineering, Finance, and Science Systems and Control We describe a framework that can integrate prior physical information, e.g., the presence of kinematic constraints, to support data-driven simulation in multi-body dynamics. Unlike other approaches, e.g., Fully-connected Neural Network (FCNN) or Recurrent Neural Network (RNN)-based methods that are used to model the system states directly, the proposed approach embraces a Neural Ordinary Differential Equation (NODE) paradigm that 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. |
| title | MBD-NODE: Physics-informed data-driven modeling and simulation of constrained multibody systems |
| topic | Computational Engineering, Finance, and Science Systems and Control |
| url | https://arxiv.org/abs/2407.08664 |