Applications of a quantum random number generator to simulations in condense matter physics

We study the importance of the quality of random numbers in Monte Carlo simulations of 2D Ising systems. Simulations are carried out at critical temperature to find the dynamic scaling law of the linear relaxation time. Our aim is to show that statistical correlations that appear in large Ising simulations performed with pseudorandom numbers can be corrected using a quantum random number generator (QRNG). To achieve high speeds and large systems, Ising lattices are simulated on a field programmable gate array (FPGA) with an optical QRNG

A FLEXIBLE AND SCALABLE EXPERIMENTATION LAYER

Modeling and simulation frameworks for use in different application domains, throughout the complete development process, and in different hardware environments need to be highly scalable. For achieving an efficient execution, different simulation algorithms and data structures must be provided to compute a concrete model on a concrete platform efficiently. The support of parallel simulation techniques becomes increasingly important in this context, which is due to the growing availability of multi-core processors and network-based computers. This leads to more complex simulation systems that are harder to configure correctly. We present an experimentation layer for the modeling and simulation framework JAMES II. It greatly facilitates the configuration and usage of the system for a user and supports distributed optimization, on-demand observation, and various distributed and non-distributed scenarios.

Cells in Silico – introducing a high-performance framework for large-scale tissue modeling

Background Discoveries in cellular dynamics and tissue development constantly reshape our understanding of fundamental biological processes such as embryogenesis, wound-healing, and tumorigenesis. High-quality microscopy data and ever-improving understanding of single-cell effects rapidly accelerate new discoveries. Still, many computational models either describe few cells highly detailed or larger cell ensembles and tissues more coarsely. Here, we connect these two scales in a joint theoretical model. Results We developed a highly parallel version of the cellular Potts model that can be flexibly applied and provides an agent-based model driving cellular events. The model can be modular extended to a multi-model simulation on both scales. Based on the NAStJA framework, a scaling implementation running efficiently on high-performance computing systems was realized. We demonstrate independence of bias in our approach as well as excellent scaling behavior. Conclusions Our model scales approximately linear beyond 10,000 cores and thus enables the simulation of large-scale three-dimensional tissues only confined by available computational resources. The strict modular design allows arbitrary models to be configured flexibly and enables applications in a wide range of research questions. Cells in Silico (CiS) can be easily molded to different model assumptions and help push computational scientists to expand their simulations to a new area in tissue simulations. As an example we highlight a 1000$^{3}$ voxel-sized cancerous tissue simulation at sub-cellular resolution

Development of a simulation and evaluation environment for a traffic flow analysis system.

A system for analysis of the traffic flow on public streets and highways through the use of Floating Car Data (FCD) relies completely on the number of simultaneously contributing vehicles, a fact that is no barrier for the phases of conception and development but poses a serious issue for the testing of such a system. Especially for smaller institutions or companies where the ability and resources to field the required number of participants is not given which in turn leads to the need for computational support to substitute the use of real vehicles by simulation. This thesis focuses on the task of the design and development of a simulation and evaluation environment for a pre-developed Traffic Flow Analysis System. The objective of this environment is to simulate the behavior of real vehicles on the existing street network including their most relevant characteristics for the purpose of congestion recognition. It is shown how simulation methods can be effectively used to create such an environment while using mathematical methods to model the characteristics of the participating system parts (vehicles) as well as the environmental influence on the external communication components (GPS, Radio)