Model Inversion by Parameter Fit Using NN Emulating the Forward Model - Evaluation of Indirect Measurements

The usage of inverse models to derive from measurements parameters of interest is wide spread in science and technology. The operational usage of many inverse models became feasible just by emulation of the inverse model via a neural net (NN). This paper shows how NN´s can be used to improve inversion accuracy by minimizing the sum of error squares. The procedure is very fast as it takes advantage of the Jacobian which is a byproduct of the NN calculation. An example from remote sensing is shown. It is also possible to take into account a non-diagonal covariance matrix of the measurement to derive the covariance matrix of the retrieved parameters.