Search Results for author:
Found 3 papers, 0 papers with code
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds
We investigate two strategies of creating these ensemble models: one that keeps the flamelet origin information (Flamelets strategy) and one that ignores the origin and considers all the data independently (Points strategy).
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
