Physics Driven Machine Learning Surrogates for Applied Aerodynamics

The resurgence of Machine Learning (ML) since 2012 has revealed visible impact in many fields, resulting in several useful products and processes shaping the Industry 4.0 agenda. This has caught the attention of computational scientists, who for the past half century or so have advanced computational continuum physics driven methods tools for the analysis and synthesis of engineered systems, facilitated primarily by fast and cheap high performance computing platforms, efficient numerical algorithms and innovative software systems. Since 2017, computational scientists have begun exploring ML in computational science and engineering and as recent as 2019/2020, in establishing an agenda for scientific machine learning which aims to build ML surrogates for predicting system performance and characteristics of engineered systems rapidly and realistically to increase productivity. This seminar will outline the prospects for scientific machine learning surrogates as alternative computational prediction tools for various problems in applied aerodynamics and their role in constructing digital twins for aerospace systems.