Inverse Modelling in Earth and Environmental Sciences
K.U. Leuven, Campus Heverlee
July 30 - August 3, 2012
An introduction to new concepts, algorithms and computational frameworks for estimating parameter, model, and predictive uncertainty from experimental data.
The course objectives are to familiarize the participants with new algorithms that have been developed to estimate model parameters from experimental data using inverse modelling. Evenly important is the estimation of the uncertainty of the estimated parameters. In inverse modelling, parameters are estimated by minimizing an objective function that quantifies the difference between the model predictions and the observations or measurements. The models that are used in earth and environmental sciences generally predict non-linear and coupled processes. The non-linearity of the models makes that the objective function may be characterised by multiple local optima. Therefore, global optimisation algorithms that search the parameter space in an efficient way are required to find a global optimum. Often, measurements of different state variables, fluxes and indirect information about the model parameters are available. In order to reconcile these different information sources with model predictions, a multi-objective optimization procedure is required. Data are afflicted with uncertainty and contaminated by ‘errors’ and a process model is always a simplified representation of reality. This uncertainty and simplification are propagated in an uncertainty of the estimated model parameters and the model predictions.
In this course, algorithms for efficient sampling of the parameter space, treating multi-objective optimisation problems, and estimating parameter and model prediction uncertainty in the presence of model error will be presented and applied in practical exercises.
Jasper Vrugt (University of California, Irvine)
Sander Huisman (Forschungszentrum Jülich)
Jan Vanderborght (Agrosphere, Forschungszentrum Jülich)
Jan Diels (Department of Earth and Environmental Sciences, KU Leuven)