Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918002391252992 |
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| author | Amer, Abdelhakim Felsager, David Brodskiy, Yury Sarabakha, Andriy |
| author_facet | Amer, Abdelhakim Felsager, David Brodskiy, Yury Sarabakha, Andriy |
| contents | Physics-informed neural networks (PINNs) integrate physical laws with data-driven models to improve generalization and sample efficiency. This work introduces an open-source implementation of the Physics-Informed Neural Network with Control (PINC) framework, designed to model the dynamics of an underwater vehicle. Using initial states, control actions, and time inputs, PINC extends PINNs to enable physically consistent transitions beyond the training domain. Various PINC configurations are tested, including differing loss functions, gradient-weighting schemes, and hyperparameters. Validation on a simulated underwater vehicle demonstrates more accurate long-horizon predictions compared to a non-physics-informed baseline |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Amer, Abdelhakim Felsager, David Brodskiy, Yury Sarabakha, Andriy Machine Learning Artificial Intelligence Robotics Physics-informed neural networks (PINNs) integrate physical laws with data-driven models to improve generalization and sample efficiency. This work introduces an open-source implementation of the Physics-Informed Neural Network with Control (PINC) framework, designed to model the dynamics of an underwater vehicle. Using initial states, control actions, and time inputs, PINC extends PINNs to enable physically consistent transitions beyond the training domain. Various PINC configurations are tested, including differing loss functions, gradient-weighting schemes, and hyperparameters. Validation on a simulated underwater vehicle demonstrates more accurate long-horizon predictions compared to a non-physics-informed baseline |
| title | Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2504.20019 |