We study optimal quantum control of the dynamics of trapped Bose-Einstein condensates: The targets are to split a condensate, residing initially in a single well, into a double well, without inducing excitation, and to excite a condensate from the ground state to the first-excited state of a single well. The condensate is described in the mean-field approximation of the Gross-Pitaevskii equation. We compare two optimization approaches in terms of their performance and ease of use; namely, gradient-ascent pulse engineering (GRAPE) and Krotov's method. Both approaches are derived from the variational principle but differ in the way the control is updated, additional costs are accounted for, and second-order-derivative information can be included. We find that GRAPE produces smoother control fields and works in a black-box manner, whereas Krotov with a suitably chosen step-size parameter converges faster but can produce sharp features in the control fields.

In order to meet the challenges of sustainable development, it is of utmost importance to involve all relevant decision makers in this process. These decision makers are diverse, including governments, corporations and private citizens. Since the latter group is the largest and the majority of decisions relevant to the future of the environment is made by that group, great effort has been put into communicating relevant research results to them. The hope is that well-informed citizens make well-informed choices and thus act in a sustainable way. However, this common but drastic simplification that more information about climate change automatically leads to pro-environmental behaviour is fundamentally flawed. It completely neglects the complex social-psychological processes that occur if people are confronted with threatening information. In reality, the defence mechanisms that are activated in such situations can also work against the goal of sustainable development, as experimental studies showed. Based on these findings, we propose an agent-based model to understand the relation between threatening climate change information, anxiety, climate change scepticism, environmental self-identity and pro-environmental behaviour. We find that the exposure to information about climate change, in general, does not increase the pro-environmental intent unless several conditions regarding the individual’s values and information density are met.

Particulate matter pollution, especially in an urban environment, is a health risk that affects many people, and the current trend shows that these problems will increase in the near future. To combat this form of air pollution, many different strategies and policies are investigated: from reducing the emission of particulate matter to finding ways of filtering the air. In this study, we explore the idea of using trees in an urban area to reduce particulate matter concentration. Since the absorption of fine dust by trees is a complex problem, influenced by many factors, we use a computer model to simulate the effect of various trees throughout the year to find the optimal candidate. We find that coniferous trees have a significant advantage, since they also absorb during the winter months, where the air quality is worse. We also conclude that a large enough area of a well-suited tree species is a feasible way to increase air quality and in some cases even to reduce the particulate matter pollution to an acceptable level.

Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an artificial neural network to depict the decision process of the agents. As a proof of principle, we use this framework to reproduce Schelling’s segregation model. We show that it is possible to use the presented framework to derive an agent-based model without the need of manually defining rules for agent behaviour. Beyond reproducing Schelling’s model, we show expansions that are possible due to the framework, such as training the agents in a different environment, which leads to different agent behaviour.