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Completed TRL 3 (started at 2, targeting 3)
We propose a real-time nowcasting capability for weather-tolerant urban air mobility (UAM) using a unique combination of state-of-the-art deep learning methods to address the challenge of predicting microweather wind fields that vary randomly over space and time. The advantage of the proposed project over existing approaches is the integration of three cutting-edge deep learning concepts into a comprehensive framework, improving over traditional methods by capturing the non-deterministic nature of microweather, making predictions that are continuous in space and time, and reducing the need for large amounts of training data.
After completing this project, the urban air mobility (UAM) program will significantly improve the accuracy and reliability of microweather prediction, mitigating the safety risks associated with unpredictable wind conditions in urban landing zones. We anticipate that the inability to find a qualified postdoc candidate may delay the project, but we plan to mitigate this risk by leveraging existing connections to AI researchers nationwide and initiating an immediate candidate search upon award notification to ensure completion of the proposed work.
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