Vehicle single track modeling using physics guided neural differential equations
<|reference_start|>Vehicle single track modeling using physics guided neural differential equations: 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.<|reference_end|>
@article{rhode2024vehicle,
    title={Vehicle single track modeling using physics guided neural differential
  equations},
    author={Stephan Rhode and Fabian Jarmolowitz and Felix Berkel},
    journal={arXiv preprint arXiv:2403.11648},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11648},
    primaryClass={cs.CE}
}