AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It was shown that the proposed PINN outperforms traditional neural networks in terms of accuracy (mean absolute errors), and the use of an attention mechanism emphasizes the role played by recent driving behavior in improving the predictive performance.
Results demonstrate the superior adaptability and robustness of the proposed TL-PINN, highlighting its potential for reliable battery state monitoring in electric vehicles.
An innovative physics-informed machine learning framework for RUL prediction and uncertainty quantification in EVDS is proposed, which effectively integrates physical information with deep learning algorithms, yielding a more concentrated probability density distribution of RUL predictions with higher accuracy, and enh...
Planning a safe, comfortable, and energy-efficient ego-vehicle trajectory is fundamental for automated driving. Efficiency reduces emissions, while safety protects occupants and vulnerable road users. Purely data-driven models, however, struggle with robustness and out-of-distribution scenarios, especially for rare edg...
This study presents a physics-informed decoupled machine learning framework integrated with a multi-objective Pareto Human–Machine Interface (HMI) advisory system that dynamically balances range extension against passenger thermal discomfort to deliver actionable driver recommendations.
Addressing the issues of traditional data-driven methods lacking interpretability and physics-informed neural network (PINN) being susceptible to noise due to statistical feature inputs, this paper proposes a feature fusion-based physics-informed neural network method for predicting the remaining useful life (RUL) of b...
Accurate battery models must generalize to unseen operating routes while supporting terminal-voltage prediction and recursive state-of-charge (SOC) estimation. This study evaluates whether physics-guided residual learning improves complete-route generalization compared with increasing equivalent-circuit-model (ECM) ord...
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