Machine learning techniques and data mining are now being used in numerous industries to analyze and forecast future trends. Exploring how machine learning and data mining may help aerodynamicists extract knowledge from CFD or experimental data to create aerodynamic coefficient estimates. High-performance airfoils and aircraft configurations are becoming increasingly relevant throughout the advanced aircraft design cycle. As a result, the development of an aerodynamic optimization approach that is paired with CFD technology and an experimental method to find the optimal aerodynamic shape and maximize aircraft performance and flight quality under certain restrictions can considerably promote aerodynamic design. Air travel has become critical to our global society. It is a global driver of economic, social, and cultural development, and it has altered how we travel, engage with others, and conduct business. It's tough to imagine a world without flying.  In the future, sustainable aviation, such as environmentally and economically friendly aviation, will be required. As a result, future aircraft performance should be more efficient, effective, and eco-friendly. Hopefully, machine learning of aerodynamic design on aircraft performance can assist find a solution to this challenge.

 Special guest : Pramudita Satria Palar, S.T, M.T, Ph.D

 Host              : Andika