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Machine learning approaches to invert the Radiative Transfer Equation
This project is no longer active.
Head: Fernando De la Torre Frade
Contact: Fernando De la Torre Frade (ftorre@cs.cmu.edu)
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Associated center: VASC
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.