PI Institution: Penn State University
Intellectual Merit: This project will investigate the use of symmetry properties over space in order to train neural networks to analyze large, complex systems distributed over space. To exploit spatial symmetry, the PI will use algebraic decomposition to obtain an analytic description of empirical data in a specific form, called the semigroup form, which involves the product of a coefficient vector and a basis set of vectors. Additionally, each component of the coefficient vector and each component of the basis set of vectors can be described individually, allowing each component to be modeled separately. A combination of RBF neural networks and time-lagged recurrent neural networks is used in implementing these principles.
Broader Impacts: The project will use, as its initial testbed, the challenge of monitoring temperature distributions across space in a boiler furnace. More accurate monitoring of boilers may be useful not only in diagnostics and maintenance but also in control; more effective boiler control can improve efficiency somewhat and reduce pollution substantially. Testbeds involving jet engines and flexible structures will also be considered. Boilers, furnaces and jet engines account for a majority of major pollutants like NOx around the world. The project will also advance crossdisciplinary education at Penn State.
