Physics-Informed Neural Network Based on Transformer Architecture for Time Series Forecasting in Engineering Systems
Abstract
Physics-Informed Neural Network Based on Transformer Architecture for Time Series Forecasting in Engineering Systems
Incoming article date: 17.07.2025The study addresses the problem of short-term forecasting of ice temperature in engineering systems with high sensitivity to thermal loads. A transformer-based architecture is proposed, enhanced with a physics-informed loss function derived from the heat balance equation. This approach accounts for the inertial properties of the system and aligns the predicted temperature dynamics with the supplied power and external conditions. The model is tested on data from an ice rink, sampled at one-minute intervals. A comparative analysis is conducted against baseline architectures including LSTM, GRU, and Transformer using MSE, MAE, and MAPE metrics. The results demonstrate a significant improvement in accuracy during transitional regimes, as well as robustness to sharp temperature fluctuations—particularly following ice resurfacing. The proposed method can be integrated into intelligent control loops for engineering systems, providing not only high predictive accuracy but also physical interpretability. The study confirms the effectiveness of incorporating physical knowledge into neural forecasting models.
Keywords: short-term forecasting, time series analysis, transformer architecture, machine learning, physics-informed modeling, predictive control