modelreeksingen

Modelreeksingen is a systematic approach used in computational science and engineering to create, analyze and compare a family of predictive models. The term, originally coined in Dutch scientific literature, refers to the generation of a sequence of models—each with slight modifications in assumptions, parameters or structural components—to explore how variations influence results. By ordering models in a series, researchers can identify which features most significantly affect performance and can thereby refine the underlying theory or computational algorithm.

The practice emerged in the 1970s within the field of numerical weather prediction, where meteorologists constructed ensembles of slightly altered dynamical models to estimate forecast uncertainty. Since then, modelreeksingen has been applied across many disciplines: climate modelling, computational fluid dynamics, econometric forecasting, and systems biology. Typical workflows involve: (1) selecting a base model and defining a set of parameters to vary; (2) generating the sequence of models; (3) running each simulation; and (4) performing statistical or sensitivity analyses on the output ensemble.

Modelreeksingen complements methods such as Bayesian model averaging and Monte Carlo simulation, offering a structured way to gauge model robustness. Its explicit focus on systematic variation makes it especially useful in situations where model choice has high stakes, such as policy decision support or safety-critical engineering. The technique continues to evolve with advances in high-performance computing and automated model calibration tools.