Vehicle single track modeling using physics guided neural differential equations
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929281304625152 |
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| author | Rhode, Stephan Jarmolowitz, Fabian Berkel, Felix |
| author_facet | Rhode, Stephan Jarmolowitz, Fabian Berkel, Felix |
| contents | In this paper, we follow the physics guided modeling approach and integrate a neural differential equation network into the physical structure of a vehicle single track model. By relying on the kinematic relations of the single track ordinary differential equations (ODE), a small neural network and few training samples are sufficient to substantially improve the model accuracy compared with a pure physics based vehicle single track model. To be more precise, the sum of squared error is reduced by 68% in the considered scenario. In addition, it is demonstrated that the prediction capabilities of the physics guided neural ODE model are superior compared with a pure black box neural differential equation approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11648 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vehicle single track modeling using physics guided neural differential equations Rhode, Stephan Jarmolowitz, Fabian Berkel, Felix Computational Engineering, Finance, and Science In this paper, we follow the physics guided modeling approach and integrate a neural differential equation network into the physical structure of a vehicle single track model. By relying on the kinematic relations of the single track ordinary differential equations (ODE), a small neural network and few training samples are sufficient to substantially improve the model accuracy compared with a pure physics based vehicle single track model. To be more precise, the sum of squared error is reduced by 68% in the considered scenario. In addition, it is demonstrated that the prediction capabilities of the physics guided neural ODE model are superior compared with a pure black box neural differential equation approach. |
| title | Vehicle single track modeling using physics guided neural differential equations |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2403.11648 |