A joint state and parameter estimation scheme for nonlinear dynamical systems

We present a novel algorithm for concurrent model state and parameter estimation in nonlinear dynamical systems. The new scheme uses ideas from three dimensional variational data assimilation (3D-Var) and the extended Kalman filter (EKF) together with the technique of state augmentation to estimate uncertain model parameters alongside the model state variables in a sequential filtering system. The method is relatively simple to implement and computationally inexpensive to run for large systems with relatively few parameters. We demonstrate the efficacy of the method via a series of identical twin experiments with three simple dynamical system models. The scheme is able to recover the parameter values to a good level of accuracy, even when observational data are noisy. We expect this new technique to be easily transferable to much larger models.

Gulliver, medium, technique

In the four Parts of Gulliver’s Travels the narrator attends closely to the manual skills, crafts and techniques of the different countries visited and to the materials and instruments by which they are mediated. The patterned, motif-like presentation of these observations and their rich contextual background, historical and literary, indicate their special significance. These references to technique play an important, previously underappreciated roll in Gulliver. They form a thematic connection between its embodied, sensual, compulsive descriptions of the world and its socio-political satire, the latter focusing on technocratic, professionalized statecraft. They are crucial to the peculiar fullness with which Swift’s writing imagines different communities of practice, different ecologies of mind.