Emulation (also denoted as metamodelling in the literature) is an important and expanding area of research and represents one of the major advances in the study of complex mathematical models, with applications ranging from model reduction to sensitivity analysis. Despite the stunning increase in computing power over recent decades, computational limitations remain a major barrier to the effective and systematic use of large-scale, process-based simulation models in rational environmental decision-making. Whereas complex models may provide clear advantages when the goal of the modelling exercise is to enhance our understanding of the natural processes, they introduce problems of model identifiability caused by over-parameterization and suffer from high computational burden when used in management and planning problems, i.e. when they are combined with optimization routines. Therefore, a combination of techniques for complex model reduction with procedures for data assimilation and learning-based control could help to bridge the gap between science and the operational use of models for decision-making.

RATTO Marco;  CASTELLETTI Andrea;  PAGANO Andrea; 

2012-06-04

ELSEVIER SCI LTD

JRC67495

1364-8152,   

http://www.sciencedirect.com/science/article/pii/S1364815211002489,    https://publications.jrc.ec.europa.eu/repository/handle/JRC67495,   

10.1016/j.envsoft.2011.11.003,