In this thesis, we investigate interactions between conical inclusions in a lipid bilayer membrane and make predictions about the patterns they form. To find these patterns, we derive an expression for the energy of a membrane as a function of the inclusion locations and search numerically for the pattern that gives minimum energy. The energy of a membrane with conical inclusions can be derived using the point particles model with corresponding formalism developed by Dommersnes and Fournier [1]. In this thesis, we apply this formalism to the finite size particles model described by Weikl et al. [2]. We compare the results of both models for a system of three inclusions, to validate the point particles model’s ability to accurately predict equilibrium patterns for conical inclusions. For most non-conical inclusions, however, the point particles model proves inadequate, leaving only the computationally intensive finite size particles model to be used for more complex inclusions. We develop a new numerical method for finding equilibrium patterns: the gradient descent method. This method is several hundred times faster than the standard Metropolis algorithm, and gives acceptable results. For large systems of inclusions, the method is very sensitive to local minima and has difficulties merging small groups. The addition of noise in the Brownian motion method proves to be unable to resolve the local minima sensitivity, but we speculate that small bursts of high noise or grouping stable inclusions structures and moving the groups as a whole may be more effective. Using the point particles model, we found that four-inclusion square-shaped structures and six-inclusion butterfly-shaped structures are favored in all systems with more than six inclusions.

As a response to the dry summer of 2018, Witteveen+Bos developed a model for water demand prediction to improve insight into water demands. Validation by water board "Hunze en Aas" has revealed the predictive power of the irrigation model to be very limited. For this thesis project, we developed a methodology for the detection of irrigation of crop parcels based on the radar vegetation index (RVI) derived from remote SAR images. This methodology can be used to improve the existing irrigation model. To achieve this, we developed a novel model to describe the evolution of a vegetation index (such as RVI) during the growth season. Unlike existing models, the model presented in this thesis includes the effect of precipitation deficit, both as a temporary inhibitor of a vegetation index, and as a long-term influence on the crop growth. The model is non-linear in many of its model parameters. Therefore, heuristic calibration methods are unavoidable. We show that the standard calibration methods non-linear least squares and differential evolution are outperformed by a hybrid of both methods that we specifically designed for this application. After calibrating the model to time series of 1167 potato parcels in the north-east of the Netherlands, we investigate different ways to cluster the model parameters. We propose explanations for three important clusterings through their RVI time series (speculative) environmental factors. Comparison with information on irrigated parcels for the years 2018-2020 reveals a statistically significant correlation between some of the clusters and irrigation. However, the variation in irrigation rate never exceeded a factor two. Therefore, no accurate classifier can be built based on these clusters. We recommend two important ways to improve the current implementation. Firstly, the baseline RVI is consistently overestimated, resulting in mostly negative normalized RVI. Because of this, the model cannot properly describe precipitation deficit-driven fluctuations in the RVI. These fluctuations are an important part of system behaviour, so improving the estimation of the baseline RVI should be the first priority for future research. Secondly, the exact irrigation dates of a set of parcels will be very useful. Comparing these dates to the corresponding RVI time series will make it possible to uncover features of the RVI evolution that are indicators of irrigation. The model parameterization can then be tuned to optimize sensitivity to these features.