| Abstract: |
| Classical system identification techniques suffer from the need to have full observations of the system in question. If instead, we couple the observations with data assimilation, and then minimize the observed error over the unknown coefficients/parameters of the proposed model, then we are able to reconstruct unknown systems from partial observations. Standard optimization routines require gradients of the system which can be addressed via adjoint methods or, as we demonstrate, when the unknown parameters lie in the observed space of the proposed model, we can utilize an asymptotic representation of the sensitivities, yielding an efficient method for parameter and hence system identification in both the linear and nonlinear setting. |
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