Now showing 1 - 10 of 12
  • Publication
    Metadata only
    Morphology-based molecular classification of spinal cord ependymomas using deep neural networks
    (Wiley, 2024-01-11) ;
    Dottermusch, Matthias
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    Schweizer, Leonille
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    Krech, Maja
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    Lempertz, Tasja
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    Schüller, Ulrich
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    Neumann, Julia E.
  • Publication
    Metadata only
    Towards auto-tuning Multi-Site Molecular Dynamics simulations with AutoPas
    (Elsevier, 2023-12-01)
    Newcome, Samuel James
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    Gratl, Fabio Alexander
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    Bungartz, Hans Joachim
    There exists an extensive literature of algorithms for short-range pairwise interactions in particle simulations, however, there is no single algorithm that performs the most optimally in every scenario, motivating the use of auto-tuning to select the optimal pairwise interaction algorithm. Previous efforts to auto-tune Molecular Dynamics have focused on Single-Site Molecular Dynamics, where the computational cost for the intermolecular force calculation is constant. Alternatively, for Multi-Site Molecular Dynamics, the cost of this calculation varies with the number of sites, which, as we show in this paper, can result in different optimal algorithms. Despite this further benefit for auto-tuning, it has yet to be applied to Multi-Site Molecules. In this paper, we introduce an implementation of Multi-Site Molecular Dynamics that is integrated with AutoPas. Using this implementation, we analyse how the relative performance between these algorithms varies as the number of sites varies, for both homogeneous and heterogeneous molecule distributions, and for two different hardware. Furthermore, we demonstrate the advantage of auto-tuning in the context of Multi-Site Molecular Dynamics using the node-level short-range particle simulation library, AutoPas.
  • Publication
    Metadata only
    MaMiCo 2.0: An enhanced open-source framework for high-performance molecular-continuum flow simulation
    (2022-12-01) ;
    Wittenberg, Helene
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    ; ;
    Maurer, Felix
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    Wittmer, Niklas
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    The macro–micro-coupling tool (MaMiCo) is an open source C++ research software framework, designed to create molecular-continuum flow simulations in a modular way, i.e. with exchangeable solvers. It can be used to couple discrete particle dynamics codes with computational fluid dynamics solvers while retaining performance, especially for parallel execution on supercomputers. We present a new version of MaMiCo that extends its functionality by a multitude of new features, notably with dynamic handling of molecular dynamics simulation instances, automated error estimation, coupling interfaces to the community codes ls1 mardyn and OpenFOAM, a Python interface, support for machine learning modules and enhanced two-way coupling. These features of the new MaMiCo version impact several fields of computational science and can be employed to tackle open research questions in the future, such as efficient multiscale numerical simulation of multi-phase flows, or fault tolerance of coupled simulations on large-scale cluster systems.
  • Publication
    Metadata only
    HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values
    (2022-06-20)
    Voß, Hannah
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    Barwikowski, Philip
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    Wurlitzer, Marcus
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    Dottermusch, Matthias
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    Schlüter, Hartmut
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    Neumann, Julia E.
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    Krisp, Christoph
    Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Proteome datasets display high technical variability and frequent missing values. Sophisticated strategies for batch effect reduction are lacking or rely on error-prone data imputation. Here we introduce HarmonizR, a data harmonization tool with appropriate missing value handling. The method exploits the structure of available data and matrix dissection for minimal data loss, without data imputation. This strategy implements two common batch effect reduction methods-ComBat and limma (removeBatchEffect()). The HarmonizR strategy, evaluated on four exemplarily analyzed datasets with up to 23 batches, demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches. Compared to data imputation methods, HarmonizR was more efficient and performed superior regarding the detection of significant proteins. HarmonizR is an efficient tool for missing data tolerant experimental variance reduction and is easily adjustable for individual dataset properties and user preferences.
  • Publication
    Metadata only
    MaMiCo: Non-local means and POD filtering with flexible data-flow for two-way coupled molecular-continuum HPC flow simulation
    (2022-05) ;
    Maurer, Felix
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    Wittenberg, Helene
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    Noise filtering in fluid dynamics enables stable, strongly-coupled molecular-continuum coupling despite potentially high thermal fluctuations. In this extended version of our conference paper “MaMiCo: Non-local Means Filtering with Flexible Data-Flow for Coupling MD and CFD” (ICCS 2021), we apply Non-Local Means filtering (NLM) in a novel space–time formulation to particle data. Our implementation in the Macro–Micro-Coupling tool (MaMiCo) features a flexible filter chain execution for HPC systems. We test it on 3D simulation data with multiple flow scenarios and continuum solvers, including a coupling to OpenFOAM. Our filtering results demonstrate that NLM not only has an excellent signal-to-noise ratio gain when extracting macroscopic flow information from particle ensembles, but also yields benefits for transient two-way coupling.
  • Publication
    Metadata only
    Transient two-way molecular-continuum coupling with openfoam and mamico: A sensitivity study
    (MDPI, 2021-12-01)
    Wittenberg, Helene
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    Molecular-continuum methods, as considered in this work, decompose the computational domain into continuum and molecular dynamics (MD) sub-domains. Compared to plain MD simulations, they greatly reduce computational effort. However, the quality of a fully two-way coupled simulation result strongly depends on a variety of system-specific parameters, and the corresponding sensitivity is only rarely addressed in the literature. Using a state-flux molecular-continuum coupling algorithm, we investigated the influences of various parameters, such as the size of the overlapping region, the coupling time step and the quality of ensemble-based sampling of flow velocities, in a Couette flow scenario. In particular, we considered a big setup in terms of domain size and number of time steps, which allowed us to investigate the long-term behavior of the coupling algorithm close to the incompressible regime. While mostly good agreement was reached on short time scales, it was the long-term behavior which differed even with slightly differently parametrized simulations. We demonstrated our findings by measuring the error in velocity, and we summarize our main observations with a few lessons learned.
  • Publication
    Metadata only
    AutoPas in ls1 mardyn: Massively parallel particle simulations with node-level auto-tuning
    (2021-03)
    Seckler, Steffen
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    Gratl, Fabio
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    Heinen, Matthias
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    Vrabec, Jadran
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    Bungartz, Hans Joachim
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    Due to computational cost, simulation software is confronted with the need to always use optimal building blocks — data structures, solver algorithms, parallelization schemes, and so forth — in terms of efficiency, while it typically needs to support a variety of hardware architectures. AutoPas implements the computationally most expensive molecular dynamics (MD) steps (e.g., force calculation) and chooses on-the-fly, i.e., at run time, the optimal combination of the previously mentioned building blocks. We detail decisions made in AutoPas to enable the interplay with MPI-parallel simulations and, to our knowledge, showcase the first MPI-parallel MD simulations that use dynamic tuning. We discuss the benefits of this approach for three simulation scenarios from process engineering, in which we obtain performance improvements of up to 50%, compared to the baseline performance of the highly optimized ls1 mardyn software.
  • Publication
    Metadata only
    Tropical cyclones in global storm-resolving models
    (2021-01-01)
    Judt, Falko
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    Klocke, Daniel
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    Rios-Berrios, Rosimar
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    Vanniere, Benoit
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    Ziemen, Florian
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    Auger, Ludovic
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    Biercamp, Joachim
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    Bretherton, Christopher
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    Chen, Xi
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    Düben, Peter
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    Hohenegger, Cathy
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    Khairoutdinov, Marat
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    Kodama, Chihiro
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    Kornblueh, Luis
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    Lin, Shian Jiann
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    Nakano, Masuo
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    Putman, William
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    Röber, Niklas
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    Roberts, Malcolm
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    Satoh, Masaki
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    Shibuya, Ryosuke
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    Stevens, Bjorn
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    Vidale, Pier Luigi
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    Wedi, Nils
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    Zhou, Linjiong
    Recent progress in computing and model development has initiated the era of global storm-resolving modeling, and with it the potential to transform weather and climate prediction. Within the general theme of vetting this new class of models, the present study evaluates nine global-storm resolving models in their ability to simulate tropical cyclones (TCs). Results indicate that, broadly speaking, the models produce realistic TCs and remove longstanding issues known from global models such as the deficiency in accurately simulating TC intensity. However, TCs are strongly affected by model formulation, and all models suffer from unique biases regarding the number of TCs, intensity, size, and structure. Some models simulated TCs better than others, but no single model was superior in every way. The overall results indicate that global storm-resolving models can open a new chapter in TC prediction, but they need to be improved to unleash their full potential.