RFA #2: Physics-Based Machine Learning Applications for Data-Driven Test
Rapidly fielding advanced aerospace systems is essential for maintaining our competitive edge, but traditional flight test techniques (FTTs), hampered by narrow data tolerances and overly conservative test points, generate excessive unproductive data and slow down the acquisition process. This research aims to revolutionize flight test by leveraging physics-based machine learning applications to reduce timelines and costs while enhancing safety, ultimately enabling faster acquisition of new manned and unmanned platforms. Collaborate with us to develop advanced data analysis tools and resources to contribute to this critical effort.
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