  This chapter reviews standard parameter-estimation techniques and presents a
novel gradient-, ensemble-, adjoint-free data-driven parameter estimation
technique in the DDDAS framework. This technique, called retrospective cost
parameter estimation (RCPE), is motivated by large-scale complex estimation
models characterized by high-dimensional nonlinear dynamics, nonlinear
parameterizations, and representational models. RCPE is illustrated by
estimating unknown parameters in three examples. In the first example, salient
features of RCPE are investigated by considering parameter estimation problem
in a low-order nonlinear system. In the second example, RCPE is used to
estimate the convective coefficient and the viscosity in the generalized
Burgers equation by using a scalar measurement. In the final example, RCPE is
used to estimate thermal conductivity coefficients that relate temporal
temperature variation with the vertical gradient of the temperature in the
atmosphere.
