Young, Peter C2015-12-10July 11-139783902823069http://hdl.handle.net/1885/68814The paper discusses the emulation of large, distributed parameter, computer models by low order, continuous-time, transfer function models obtained using the SRIVC method of identification and estimation for continuous-time models. This yields a minimally parameterized, reduced order, 'nominal' emulation model that often reproduces the dynamic behavior of the large model to a remarkable degree. In full Dynamic Model Emulation (DEM), the objective is to emulate the high order model over a whole, user-defined range of parameter values, so that it can act as a surrogate for the high order model in applications that demand fast, repeated solution, as in Monte Carlo simulation and sensitivity analysis, or be used as a low order model in automatic control system design and adaptive forecasting applications. Most of the paper deals with the 'stand-alone' emulation of two high order, distributed parameter, computer models for the transport and dispersion of solutes in water systems.Keywords: Adaptive forecasting; Computer models; Continuous time; Continuous time models; Dispersion of solutes; Distributed parameter; Dynamic behaviors; Emulation model; High order model; Low order; Low order models; Monte Carlo Simulation; Parameter values; Para201210.3182/20120711-3-BE-2027.000992016-02-24