A Hybrid Predictor–Corrector Decoupled Method Based on Operator Learning for Solving Interface Problems

Key Points

• The hybrid method enhances accuracy by refining predictions through operator learning, and solves interface problems effectively.
• The approach combines a predictor-corrector mechanism with operator learning for optimal results in computational applications.
• Analysis utilized a mathematical framework for operator learning to address complex interface problems effectively.
• This method may enable faster computations in fields requiring interface problem solving, supporting diverse applications.