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Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate
In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest(QoI) remains a challenge.
Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning
Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise.
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