iLMformer: A data-driven hybrid architecture for multivariate time series parameter forecasting of nuclear engineering systems

Key Points

• Forecasting parameters with a hybrid architecture enhances accuracy in nuclear engineering systems.
• The model effectively utilizes multivariate time series data to improve predictions for complex systems.
• Observational analysis applies data-driven methods specifically for forecasting in nuclear engineering.
• These findings support the potential for increased efficiency and safety in nuclear engineering applications.