Continuous-Time Deep Learning for Climate Forecasting: Neural ODEs Applied to Benghazi Temperature Data Raja Mohammad Elbarjo (Comprehensive Journal of Science, 2025)
This work discusses using Neural Ordinary Differential Equations (ODEs) forforecasting temperature values. We discuss the impact of learning rate reduction on performancein the model, and display impressive gains in training loss and test loss after the learning rate wasreduced from 0.075 to 0.001. This study highlights the importance of learning rate tuning forbetter model generalization and more accurate predictions. We also provide a detailed descriptionof the data preprocessing steps, the model architecture, and the results of the experiments,including comparisons of the performance before and after the adjustment.
Keywords: Neural Ordinary Differential Equations, Weather Prediction, Deep Learning, Benghazi, Neural Networks, Temperature