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Density Estimation for Entry Guidance Problems using Deep Learning
This work presents a deep-learning approach to estimate atmospheric density profiles for use in planetary entry guidance problems.
Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables.
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