  One of the critical components in Industrial Gas Turbines (IGT) is the
turbine blade. Design of turbine blades needs to consider multiple aspects like
aerodynamic efficiency, durability, safety and manufacturing, which make the
design process sequential and iterative.The sequential nature of these
iterations forces a long design cycle time, ranging from several months to
years. Due to the reactionary nature of these iterations, little effort has
been made to accumulate data in a manner that allows for deep exploration and
understanding of the total design space. This is exemplified in the process of
designing the individual components of the IGT resulting in a potential
unrealized efficiency. To overcome the aforementioned challenges, we
demonstrate a probabilistic inverse design machine learning framework (PMI), to
carry out an explicit inverse design. PMI calculates the design explicitly
without excessive costly iteration and overcomes the challenges associated with
ill-posed inverse problems. In this work, the framework will be demonstrated on
inverse aerodynamic design of three-dimensional turbine blades.
