AW-EL-PINNs: A Multi-Task Learning Physics-Informed Neural Network for Euler-Lagrange Systems in Optimal Control Problems
By: Chuandong Li, Runtian Zeng
Potential Business Impact:
Makes computers solve hard problems faster, more accurately.
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.
Similar Papers
Lyapunov-Based Physics-Informed Deep Neural Networks with Skew Symmetry Considerations
Systems and Control
Makes robots move more smoothly and accurately.
Self-adaptive weighting and sampling for physics-informed neural networks
Machine Learning (Stat)
Makes computer math solving faster and more accurate.
Self-adaptive weighting and sampling for physics-informed neural networks
Machine Learning (Stat)
Makes computer math solving faster and more accurate.