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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.25262 |
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| _version_ | 1866909815072096256 |
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| author | Li, Chuandong Zeng, Runtian |
| author_facet | Li, Chuandong Zeng, Runtian |
| contents | This paper presents adaptive weighted Euler-Lagrange theorem combined physics-informed neural networks (AW-EL-PINNs) for solving Euler-Lagrange systems in optimal control problems. The framework systematically converts optimal control frameworks into two-point boundary value problems (TPBVPs) while establishing a multi-task learning paradigm through innovative integration of the Euler-Lagrange theorem with deep learning architecture. An adaptive loss weighting mechanism dynamically balances loss function components during training, decreasing tedious manual tuning of weighting the loss functions compared to the conventional physics-informed neural networks (PINNs). Based on six numerical examples, it's clear that AW-EL-PINNs achieve enhanced solution accuracy compared to baseline methods while maintaining stability throughout the optimization process. These results highlight the framework's capability to improve precision and ensure stability in solving Euler-Lagrange systems in optimal control problems, offering potential strategies for problems under physical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AW-EL-PINNs: A Multi-Task Learning Physics-Informed Neural Network for Euler-Lagrange Systems in Optimal Control Problems Li, Chuandong Zeng, Runtian Numerical Analysis Systems and Control This paper presents adaptive weighted Euler-Lagrange theorem combined physics-informed neural networks (AW-EL-PINNs) for solving Euler-Lagrange systems in optimal control problems. The framework systematically converts optimal control frameworks into two-point boundary value problems (TPBVPs) while establishing a multi-task learning paradigm through innovative integration of the Euler-Lagrange theorem with deep learning architecture. An adaptive loss weighting mechanism dynamically balances loss function components during training, decreasing tedious manual tuning of weighting the loss functions compared to the conventional physics-informed neural networks (PINNs). Based on six numerical examples, it's clear that AW-EL-PINNs achieve enhanced solution accuracy compared to baseline methods while maintaining stability throughout the optimization process. These results highlight the framework's capability to improve precision and ensure stability in solving Euler-Lagrange systems in optimal control problems, offering potential strategies for problems under physical applications. |
| title | AW-EL-PINNs: A Multi-Task Learning Physics-Informed Neural Network for Euler-Lagrange Systems in Optimal Control Problems |
| topic | Numerical Analysis Systems and Control |
| url | https://arxiv.org/abs/2509.25262 |