Physics-informed machine learning framework for boiling heat transfer prediction of dielectric fluids

Key Points

• Boiling heat transfer predictions improve with a physics-informed machine learning framework, offering increased accuracy.
• Key evidence shows a notable reduction in prediction errors when applying the new predictive modeling approach.
• Assessment using numerical simulation provides insights into the behavior of dielectric fluids under boiling conditions.
• This model highlights potential applications in advanced cooling systems, yet further validation in real-world scenarios is necessary.