Abstract: The aim of this workshop is to integrate results of different domains of computer science, computational science and mathematics. We invite papers oriented toward simulations, either hard simulations by means of finite element or finite difference methods, or soft simulations by means of evolutionary computations, particle swarm optimization and other. The workshop is most interested in simulations performed by using agent-oriented systems or by utilizing adaptive algorithms, but simulations performed by other kind of systems are also welcome. Agent-oriented system seems to be the attractive tool useful for numerous domains of applications. Adaptive algorithms allow significant decrease of the computational cost by utilizing computational resources on most important aspect of the problem. This year following the challenges of ICCS 2015 theme "Computational Science at the Gates of Nature" we invite submissions using techniques dealing with large simulations, e.g. agents based algorithms dealing with big data, model reduction techniques for large problems, fast solvers for large three dimensional simulations, etc. To give - rather flexible - guidance in the subject, the following, more detailed, topics are suggested. These of theoretical brand, like: (a) multi-agent systems in high-performance computing, (b) efficient adaptive algorithms for big problems, (c) low computational cost adaptive solvers, (d) agent-oriented approach to adaptive algorithms, (e) model reduction techniques for large problems, (f) mathematical modeling and asymptotic analysis of large problems, (g) finite element or finite difference methods for three dimensional or non-stationary problems, (h) mathematical modeling and asymptotic analysis. And those with stress on application sphere: (a) agents based algorithms dealing with big data, (b) application of adaptive algorithms in large simulation, (c) simulation and large multi-agent systems, (d) application of adaptive algorithms in three dimensional finite element and finite difference simulations, (e) application of multi-agent systems in computational modeling, (f) multi-agent systems in integration of different approaches.

Abstract: In this work, we present a novel isogeometric analysis discretization for the Navier-Stokes-Cahn-Hilliard equation, which uses divergence-conforming spaces. Basis functions generated with this method can have higher-order continuity, and allow to directly discretize the higher-order operators present in the equation. The discretization is implemented in PetIGA-MF, a high-performance framework for discrete differential forms. We present solutions in a two-dimensional annulus, and model spinodal decomposition under shear flow.

Abstract: Cylindrical Petri dishes embedded in a rectangular waveguide and exposed to a polarized electromagnetic wave are often used to grow cell cultures. To guarantee the success of these cultures, it is necessary to enforce that the specific absorption rate distribution is sufficiently high and uniform over the Petri dish. Accurate numerical simulations are needed to design such systems. These simulations constitute a challenge due to the strong discontinuity of electromagnetic parameters of the materials involved, the relative low value of field within the dish cultures compared with the rest of the domain, and the presence of the meniscus shape developed at the liquid/solid interface. The latter greatly increases the level of complexity of the model in terms of geometry and the intensity of the gradients/singularities of the field solution. In here, we employ a three-dimensional (3D) $hp$-adaptive finite element method using isoparametric elements to obtain highly accurate simulations. We analyse the impact of the geometrical modeling of the meniscus shape cell culture in the $hp$-adaptivity. Numerical results concerning the convergence history of the error indicate the numerical difficulties arisen due to the presence of a meniscus-shaped object. At the same time, the resulting energy distribution shows that to consider such meniscus shape is essential to guarantee the success of the cell culture from the biological point of view.

Abstract: Scientific workflows in clouds have been successfully used for automation of large-scale computations, but so far they were applied to the loosely-coupled problems, where most workflow tasks can be processed independently in parallel and do not require high volume of communication. The multi-frontal solver algorithm for finite element meshes can be represented as a workflow, but the fine granularity of resulting tasks and the large communication to computation ratio makes it hard to execute it efficiently in loosely-coupled environments such as the Infrastructure-as-a-Service clouds. In this paper, we hypothesize that there exists a class of meshes that can be effectively decomposed into a workflow and mapped onto a cloud infrastructure. To show that, we have developed a workflow-based multi-frontal solver using the HyperFlow workflow engine, which comprises workflow generation from the elimination tree, analysis of the workflow structure, task aggregation based on estimated computation costs, and distributed execution using a~dedicated worker service that can be deployed in clouds or clusters. The results of our experiments using the workflows of over 10,000 tasks indicate that after task aggregation the resulting workflows of over 100 tasks can be efficiently executed and the overheads are not prohibitive. These results lead us to conclusions that our approach is feasible and gives prospects for providing a generic workflow-based solution using clouds for problems typically considered as requiring HPC infrastructure.

Abstract: We present an application of Ant Colony Optimisation (ACO) to simulate socio-cognitive features of a population. We incorporated perspective taking ability to generate three different proportions of ant colonies: Control Sample, High Altercentricity Sample, and Low Altercentricity Sample. We simulated their performances on the Travelling Salesman Problem and compared them with the classic ACO. Results show that all three 'cognitively enabled' ant colonies require less time than the classic ACO. Also, though the best solution is found by the classic ACO, the Control Sample finds almost as good a solution but much faster. This study is offered as an example to illustrate an easy way of defining inter-individual interactions based on stigmergic features of the environment.