Machine learning approaches to invert the Radiative Transfer Equation

Estimation of the constituents of a gas (e.g. temperature, concentration) from high resolution spectroscopic measurements is a fundamental step to control and improve the efficiency of combustion processes governed by the Radiative Tranfer Equation (RTE). A machine learning approach is followed to learn a mapping between the spectroscopic measurements and gas constituents such as temperature, concentration and length. This is a challenging problem due to the non-linear behavior of the RTE and the high dimensional data obtained from sensor measurements. Several extensions of supervised and dimensionality reduction techniques are being evaluated.