We apply Big data analytics to enable optimal decision making in complex systems that are dynamic and dependent on real-time data. Engineers can use big data in their design work as valuable guidance. Spotting patterns of success and failure from the past data in a dynamic real-time environment brings a new dimension in design optimization. A computer in a rocket using big data can autonomously decide its next course of action by matching patterns from the past that worked. Cybersecurity applications in aviation can use big data predictive analytics to initiate preventive actions to protect an aircraft. Using predictive patterns from the past, an autonomous system can make intelligent decisions in a challenging dynamic environment. Big data analytics can crunch massive quantities of real-time data and reliably balance safety, security and efficiency.
Airlines are adopting big data analytics to maximize operational efficiency, minimize cost and enhance security. Computational fluid dynamics systems continue to manage the vast amounts of data generated by current and future large-scale simulations. Aerospace industry, research, and development are impacted profoundly by the big data revolution.

The effective use of very large amounts of data generated by computational fluid dynamics is critical to advancing aerospace technologies. We use Big data predictive analytic tools for analyzing large CFD-generated data sets to immensely improve the overall aerodynamic design and analysis process. With the advent of more powerful computing systems, big data predictive analytics is enabling a single CFD simulation to solve for the flow about complete aerospace systems, including simulations of space vehicle launch sequences, aircraft with full engines and aircraft in flight maneuvering environments.

Typical applications include:

    1. Aircraft engine diagnostics

    2. Airline operations.

    3. Big data predictive analytic tools for analyzing large CFD-generated data sets

    4. Use of deep learning techniques in predictive analytics for Aviation cybersecurity.

    5. Interactive real time visual analytics for diagnostic, descriptive, prescriptive, and predictive monitoring of complex integrated systems.

    6. Big data analytics for tracking flight track anomalies

    7. Inversed Reinforced learning to identify precursors to Flight Track Anomalies in aerospace systems.

    8. Deep and Multiple Kernel Learning for heterogeneous anomaly detection in complex aviation and aerospace systems.