We use a relatively recent nonlinear manifold learning technique (diffusion maps) to parameterize low dimensional attracting manifolds arising in the description of detailed chemical kinetics mechanisms. With no a priori knowledge about the shape and dimension of the manifold, such an approach provides a way of solving a reduced (and less stiff) set of equations in terms of automatically detected slow variables. Advantages as well as disadvantages of the approach are discussed.

A manifold learning approach to model reduction in combustion / Chiavazzo, Eliodoro; William C., Gear; Benjamin E., Sonday; Ioannis G., Kevrekidis. - (2013). ((Intervento presentato al convegno 4th International Workshop on Model Reduction in Reacting Flows tenutosi a San Francisco (CA, USA) nel 19-21 Giugno 2013.

A manifold learning approach to model reduction in combustion

CHIAVAZZO, ELIODORO;
2013

Abstract

We use a relatively recent nonlinear manifold learning technique (diffusion maps) to parameterize low dimensional attracting manifolds arising in the description of detailed chemical kinetics mechanisms. With no a priori knowledge about the shape and dimension of the manifold, such an approach provides a way of solving a reduced (and less stiff) set of equations in terms of automatically detected slow variables. Advantages as well as disadvantages of the approach are discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2557746
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