Machine Learning and AI Applications to Support Digital Engineering and Digital Twin Aerospace Applications

This presentation explores the emergent field of Digital Engineering and its potential applications in the Aerospace industry, particularly through the concept of Digital Twins. An extensive overview of three types of Digital Twins - Data Driven, Physics Based Data Driven, and Hybrid Data Driven - is provided, with an emphasis on their respective data types, sources, machine learning techniques, applications, and implementation times.

The first type, Data Driven, is grounded in real-world data collected via onboard sensors and is ideal for repetitive operations, such as commercial aircraft predictive maintenance. The second type, Physics Based Data Driven, leverages synthetic physics-based simulation data, offering promising prospects for both current and future aircraft operations. The third type, Hybrid Data Driven, combines both real-world and synthetic data, reducing the need for physical sensors and enabling optimized design definition. The presentation concludes by recommending the use of Digital Engineering and Virtual Environments for generating Physics-Based synthetic training datasets for machine learning and AI applications in aerospace. The benefits of such an approach are explored, including the ability to predict future strategies for aircraft sustainment, autonomous flight, future mission planning, and enhanced aircraft designs.

Presented by Dr. Gerardo Olivares I Senior Research Scientist & Director I NIAR, Wichita State University, at the ATCx AI for Engineers in July 2023, 20 mins

The presentation is in English by default. The following languages are available by clicking on Audio Description ('AD') on the video screen: CN, KR, JP, ES, and PT.