Abstract: During the last decade the techniques of complex network analysis have found application in climate research. The main idea consists in embedding the characteristics of climate variables, e.g., temperature, pressure or rainfall, into the topology of complex networks by appropriate linear and nonlinear measures. Applying such measures on climate time series leads to defining links between their corresponding locations on the studied region, whereas the locations are the network’s nodes. The resulted networks, consequently, are analysed using the various network analysis tools present in literature in order to get a better insight on the processes, patterns and interactions occurring in climate system. In this regard we present ClimNet; a complete set of software tools to construct climate networks based on a wide range of linear (cross correlation) and nonlinear (Information theoretic) measures. The presented software will allow the construction of large networks’ adjacency matrices from climate time series while supporting functions to tune relationships to different time-scales by means of symbolic ordinal analysis. The provided tools have been used in the production of various original contributions in climate research. This work presents an in-depth description of the implemented statistical functions widely used to construct climate networks. Additionally, a general overview of the architecture of the developed software is provided as well as a brief analysis of application examples.

Abstract: This paper investigates the relationship between the underlying complexity of urban agent-based models and the performance of optimisation algorithms. In particular, we address the problem of optimal green space allocation within a densely populated urban area. We find that a simple monocentric urban growth model may not contain enough complexity to be able to take complete advantage of advanced optimisation techniques such as Genetic Algorithms (GA) and that, in fact, simple greedy baselines can find a better policy for these simple models. We then turn to more realistic urban models and show that the performance of GA increases with model complexity and uncertainty level.

Abstract: Carbon nanotubes possess many interesting properties, which make them a promising material for a variety of applications. In this paper, we present a unified framework for the simulation of mechanical behavior of carbon nanotubes. It allows the creation, simulation and visualization of these structures, extending previous work by the research group ”MISMO” at TU Darmstadt. In particular, we develop and integrate a new iterative solving procedure, employing the conjugate gradient method, that drastically reduces the memory consumption in comparison to the existing approaches. The increase in operations for the memory saving approach is partially offset by a well scaling shared-memory parallelization. In addition the hotspots in the code have been vectorized. Altogether, the resulting simulation framework enables the simulation of complex carbon nanotubes on commodity multicore desktop computers.

Abstract: Mechatronic systems play a significant role in different types of industry, especially in transportation, aerospace, automotive and manufacturing. Although their multidisciplinary nature provides enormous functionalities, it is still one of the substantial challenges which frequently impede their design process. Notably, the conceptual design phase aggregates various engineering disciplines, project and business management fields, where different methods, modeling languages and software tools are applied. Therefore, an integrated environment is required to intimately engage the different domains together. This paper outlines a model-based research approach for an integrated conceptual design evaluation of mechatronic systems using SysML. Particularly, the state of the art is highlighted, most important challenges, remaining problems in this field and a novel solution is proposed, named SysDICE, combining model based system engineering and artificial intelligence techniques to support for achieving efficient design.

Abstract: Computational Science tackles complex problems by definition. These problems concern people not only in large scale, but in their day-to-day life. With the development of computing facilities, novel application areas can legitimately benefit from the existing experience in the field. Nevertheless, the lack of reusability, the growing in complexity, and the “computing-oriented” nature of the actual solutions call for several improvements. Among these, raising the level of abstraction is the one we address in this paper. As an illustration we can mention the problem of the validity of the experimentations which depends on the validity of the defined programs (bugs not in the experiment and data but in the simulators/validators!). This raise the needs for leveraging on knowledge / expertise. In the software and systems modeling community, research on domain-specific modeling languages (DSMLs) is focused since the last decade on providing technologies for developing languages and tools that allow domain experts to develop system solutions efficiently. In this vision paper, based on concrete experiments, we claim that DSMLs can bridge the gap between the (problem) space in which scientist work and the implementation (programming) space. Incorporating domain-specific concepts and high-quality development experience into DSMLs can significantly improve scientist productivity and experimentation quality.