A moving subinterval decomposition analysis method for non-probabilistic convex model under uncertainty

Key Points

• The moving subinterval decomposition method effectively analyzes non-probabilistic convex models under uncertainty.
• This method enhances model accuracy and provides crucial insights into uncertainty analysis for complex systems.
• Assessment using moving subinterval decomposition across various convex models highlights its adaptability and precision.
• The findings indicate significant implications for optimizing model performance, requiring further validation in diverse applications.