Neural Partial Differential Method for Extracting Aerodynamic Derivatives from Flight Data

the partial differential of neural output is suggested and demonstrated. The performance of the two approaches (namely, the finite difference and the partial differential approach) is evaluated in terms of the estimated aerodynamic derivatives. It is shown that the two methods give comparable results; however, the partial differential approach provides first-order aerodynamic derivatives more accurately. Moreover, the results of the partial differentialapproacharecomparablewiththeresultsobtainedfromtheequationerrormethod.Moreover,gradient descent and scaled conjugate gradient methods are evaluated in terms of the mean square error for training the neural network. The proposed partial differential approach does not suffer from numerical instability and provides higher-orderderivatives;therefore,itcanbeextendedtothenonlinearaerodynamicregimeandtounstableaircraft.