Machine Learning for Thermal Fluid systems

 Use of machine learning for thermal-fluids applications can result in transformative benefits, yet it has been vastly under-explored. Our group is exploring the use of machine learning-based algorithms and statistical modeling as alternatives for physics-based models for complex, multiphysics problems. Specific efforts in this direction include:

• Predicting thermally-induced failures in microelectronics packages
  
• Predictions of wettability
• Understanding dropwise condensation