PUMA publications for /group/researchcode/simulationMon Mar 30 17:00:01 CEST 2026Applied Sciences31608Ontology-based production simulation with ontologysim122022forschungsdaten metadata ontologie simulation tools Tue Oct 07 10:47:17 CEST 2025Data Science Journal09Enhancing Multiscale Simulation Data Management with Domain Ontologies and an ELN: Addressing Challenges and Implementing Strategies2025metadata ontologie simulation nfdi4ing multiscale Data plays a fundamental role in advancing knowledge and driving research across all fields. Despite the abundance of data, several challenges still need to be addressed. These challenges include limited accessibility, heterogeneous data, lack of interconnections between associated topics, difficulty retrieving required information, semantic mismatches, and several other challenges. Ontologies provide a structured method to address these challenges effectively. This study addresses two central aspects of ontology research. First, it details the multidisciplinary ontology development process, highlighting the challenges, mitigation strategies, and impacts on domain data management. It then offers guidelines for beginners and individuals with a background in data management on effective engagement in ontology creation. Second, it introduces the Ontology for Multiscale Simulation methods (Onto-MS), constructed by following the guidelines from the first part. The ontology, developed in Web Ontology Language (OWL) using Protégé, integrates with other ontologies, such as the Elementary Multiperspective Material Ontology (EMMO), aligning this research with the Linked Data concept. A custom Python script was used to incorporate the ontology into an Electronic Laboratory Notebook (ELN), enabling the automatic creation of knowledge graphs and systematic data organization conforming to the ontology. This research successfully answers the fundamental questions in interdisciplinary or domain-level ontology development. Onto-MS provides a robust framework for organizing and linking data in multiscale simulations within computational materials science. Furthermore, ontology incorporation into an ELN simplifies its integration into data management practices. While ontology development is ongoing, the current version is functional and continuously refined with new insights and feedback.Thu Dec 05 14:42:20 CET 2024Journal of Computational Science102291OpenDiHu: An efficient and scalable framework for biophysical simulations of the neuromuscular system792024tool hpc-computing neuromuscular simulation The versatile neuromuscular system, consisting of skeletal muscles and the nervous system, enables human to perform crucial everyday tasks. To investigate its functioning and dysfunctioning with computer simulations, highly resolved, multi-scale models are favorable, whose numerical solutions demand for high performance computing. We present OpenDiHu, a versatile, high-performance computing, open source software framework for detailed, systemic simulations of skeletal muscles and their recruitment mechanisms. OpenDiHu allows to solve a variety of multi-scale models, including 3D muscle mechanics, measurable electromyographic signals, action potential propagation in the muscle tissue, subcellular bio-chemo-electrical processes, and the neural drive to the muscle. All these components can be combined with a wide range of numerical solution schemes into comprehensive simulation setups for the entire system. Experiments on up to almost 27000 cores demonstrate the efficiency and parallel scalability of OpenDiHu. This enables in silico experiments at very high spatial and temporal resolutions.Tue Mar 10 15:16:30 CET 2020Proceedings of the 2011 Winter Simulation Conference (WSC)122909-2920SoPT: Ontology for simulation optimization for scientific experiments2011forschungsdaten metadata optimization ontologie simulation Simulation optimization is attracting increasing research interest from the modeling and simulation community. Although there is much research on how to apply various simulation optimization techniques to solve numerous practical and research problems, researchers find that existing optimization routines are difficult to extend or integrate and often require one to develop their own optimization methods because the existing ones are problem-specific and not designed for reuse. In order to facilitate reuse of the available optimization routines and better capture the essence of different simulation optimization techniques, an ontology for simulation optimization (SoPT) is devised. SoPT includes concepts from both conventional optimization/mathematical programming and simulation optimization. Represented in ontological form, optimization routines can also be transformed into actual executable application code (e.g., targeting JSIM or ScalaTion). As illustrative examples, SoPT is being applied to real scientific computational problems.Fri Feb 16 09:39:19 CET 2018International Conference on Theory and Practice of Digital Libraries140--151Challenges of Research Data Management for High Performance Computing2017forschungsdaten metadata management data simulation obib hpc This paper targets the challenges of research data management with a focus on High Performance Computing (HPC) and simulation data. Main challenges are discussed: The Big Data qualities of HPC research data, technical data management, organizational and administrative challenges. Emerging from these challenges, requirements for a feasible HPC research data management are derived and an alternative data life cycle is proposed. The requirement analysis includes recommendations which are based on a modified OAIS architecture: To meet the HPC requirements of a scalable system, metadata and data must not be stored together. Metadata keys are defined and organizational actions are recommended. Moreover, this paper contributes by introducing the role of a Scientific Data Manager, who is responsible for the institution’s data management and taking stewardship of the data.Tue Jul 18 09:52:14 CEST 2017Concurrency and Computation: Practice and Experience101744-1759Standards-based metadata management for molecular simulations.262014forschungsdaten metadata molecular simulation itt engineering Tue Jul 18 09:33:00 CEST 2017SwitzerlandWhither turbulence and big data in the 21st century?28509-515Public Dissemination of Raw Turbulence Data2017bigdata forschungsdaten reuse dissemination simulation obib engineering iag It is argued that there is a certain urgency to the discussion of whether raw data should be made publicly available within the turbulence community, and about the best ways, technology and rules for possible dissemination. Besides expressing the personal opinion that such sharing would be advantageous for the field, the urgency mostly arises from the danger that funding agencies or other institutions would otherwise set standards without proper community input. This paper is in part a plea for community action in that direction. As an example, the experience of the Madrid school of Aeronautics with the dissemination of numerical simulation results is briefly reviewed, including the present technological solutions and usage statistics.