Mars Lander Aerodynamic Model Data Fusion with Machine Learning (AEROFUSION-MLUQ)

In this study, we propose to develop and test methods for fusing aerodynamic data using machine learning with embeded uncertainty quantification. We will partner with USC, NCSU, and UF to combine reduced order modeling and machine learning techniques to fuse aerodynamic data from various sources of varying fidelity into a mathematical model that includes uncertainty bounds.

Propagating aerodynamic uncertainties in a statistically rigorous fashion should allow for less conservative uncertainty bounds, reduced reliance on high-fidelity data, and reduced computational time due to efficient use of surrogate modeling. This should result in less conservative control law designs, tighter landing dispersions for EDL vehicles, and reduced analysis costs.

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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