Current first-principles models of complex
chemistry, such as combustion reaction networks,
often give inaccurate predictions of the
time variation of chemical species. Moreover,
the high complexity and dimensionality of these
models render them impractical for real-time
prediction and control of chemical network processes.
These limitations have motivated us to
search for an alternative paradigm that is able
to both identify the correct model from the observed
dynamical data and reduce complexity
while preserving the underlying network structure.
We want to determine at the same time:
- Network structure.
- Reaction rate constants.
- Initial state.