Hybrid Artificial Intelligence Techniques for Multiphysics Heat Transfer Problems

Multiphysics heat transfer problems involve complex interactions among different physical processes, including conduction, convection, radiation, and fluid flow. Traditional numerical methods often face high computational costs and lengthy runtimes. Hybrid artificial intelligence techniques that mix data-driven models with physics-based solvers have emerged as a strong alternative. This chapter examines how various artificial intelligence models can be integrated, identifies key challenges in data collection and model validation, and proposes solutions to enhance accuracy and generalization. Case studies are included to showcase the effectiveness of hybrid artificial intelligence techniques in real-world heat transfer applications.