Robotics Institute: Machine learning approaches to invert the Radiative Transfer Equation


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[Project image] 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This project is no longer active.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

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Project Description

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.

Past members

Name Title Email Address

Fernando De la Torre Frade Research Scientist ftorre@cs.cmu.edu

Esteban Garcia Visiting Scholar

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Name	Title	Email Address
Fernando De la Torre Frade	Research Scientist	ftorre@cs.cmu.edu
Esteban Garcia	Visiting Scholar