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Trapped Solitary Waves in a Periodic External Force: A Numerical Investigation using the Whitham Equation and the Sponge Layer Method
<p>Supplementary Videos.</p>
Numerical data for "The Timescales of Quantum Breaking"
<p>This is the numerical data that belongs to the paper</p> <p>M. Michel, S. Zell, <em><strong>The Timescales of Quantum Breaking, </strong></em><a href="https://arxiv.org/abs/2306.09410">arXiv:2306.09410</a>.</p> <p>The code used to produce this numerical data can be reviewed in the <a href="https://github.com/marco-michel/QuantumBreaking-TimeScales">QuantumBreaking-TimeScales</a> repository on github.</p> <p>This code relies on the computer program <strong><em>TimeEvolver</em></strong>, which was presented in</p> <p>M. Michel, S. Zell, <em>TimeEvolver: A Program for Time Evolution With Improved Error Bound, </em><a href="https://doi.org/10.1016/j.cpc.2022.108374">Comput. Phys. Commun. <strong>277</strong> (2022) 108374</a>, <a href="https://arxiv.org/abs/2205.15346">arXiv:2205.15346</a>.</p> <p> </p> <p>In the following we outline the procedure to generate the data and plots presented in this paper. All equation numbers refer to the latest version of <a href="https://arxiv.org/abs/2306.09410">arXiv:2306.09410</a>.</p> <p> </p> <p>In this work we studied the dynamics of a multi-mode quantum system defined by eq. (3.6) and how the timescale of evolution depends on its defining parameters <span class="math-tex">\(N, Q, C_m\)</span> and <span class="math-tex">\(\lambda\)</span>. To extract a quantitative scaling, all but one of these are kept fixed while the remaining one is varied over a certain interval. For each choice of parameters, the exact time evolution is computed numerically using the <strong>TimeEvolver</strong> software package.</p> <p> </p> <p>We consider three parameter regimes, each with a corresponding folder, namely <em>underCritical </em>(defined by eq. (4.4)), <em>overCriticalFewSpecies </em>(defined by eq. (4.13)) and <em>overCriticalManySpecies </em>(defined by eq. (4.19)). Every folder is divided into subfolders, where the name of the subfolder indicates the parameter that is varied. (As a concrete example, in <em>undercritical/Cm</em>, all parameters except for Cm are set by eq. (4.4) and the system is studied for different values of Cm.) In some cases, an additional parameter deviates from the values as set by eqs. (4.4), (4.13) or (4.19). This is indicated with an underscore followed by the name of the parameter and its value. In addition to that, there are also subfolders named <em>realTimePlots </em>containing data illustrating the real time evolution in different dynamical phases.</p> <p>Apart from the three parameter regimes described above, we also study the system for the parameter choice of eq. (4.25) in the folder <em>interpolating</em>. Finally, we also investigate a different system defined by eq. (2.35) in the folder <em>PPM</em>.</p> <p>Beside the raw data in the form of <strong>h5</strong> files each folder contains a Mathematica-notebook which is responsible for data analysis and producing the plots presented in the paper. These have specific adaptions for each parameter and are named accordingly.</p> <p>To extract a dynamic timescale we prepare the system in a specific initial state (given by eq. (2.10)) and<br> track the time it takes for the system to move "sufficiently far" away from it. In order to quantify this, we evaluate in each simulation the maximal deviation of the <span class="math-tex">\(n_0\)</span>-mode from its initial value (see eq. (4.5)). Then we determine the minimal relative excursion over all runs in the scan range and after multiplying by a factor of 0.8, this sets the threshold at which we extract a time duration for each run (see eq. (4.6).</p> <p>Finally, we fit these values with various fit functions to determine quantitatively how the timescale of evolution depend on the original parameters <span class="math-tex">\(N, Q, C_m\)</span> and <span class="math-tex">\(\lambda\)</span>.</p> <p>We refer to the paper for a comprehensive description as well as further details.</p>
Appendix The benchmark values are used in numerical experiments.
<p>Appendix The benchmark values are used in numerical experiments.</p>
H2020 ENODISE: ONERA Numerical Aeroacoustic Database Configuration B1
<p>The ONERA numerical database on configuration B1 deals with the scattering effects of 3 side-by-side puller propellers installed upstream a wing profile in a flow, using an in-house 3-steps-chaining-method. The aim of this work has been to evaluate the ability of the method to predict the scattering effects on the sound pressure level and its directivity.</p> <p><br> The two operating points that have been studied are:<br> • U∞ = 30 m/s, J = 0.8, AoA = 2°, with a blade phase-angle Φ=0° between the middle and the side propellers<br> • U∞ = 30 m/s, J = 0.8, AoA = 2°, with a blade phase-angle Φ=10° between the middle and the side propellers</p> <p>The present configuration follows the one described in TUD experimental contribution for configuration B1.</p> <p>A synthetized description of the numerical method including some important details and results will be included in the file ONERA_B1_configuration.pptx.</p> <p>The ONERA database contains numerical results about aerodynamic performances (thrust, torque and efficiency). It also includes pressure and sound pressure levels on the arcs microphones positions of TUD experimental configuration.<br> Data are stored in .dat and .plt format files.</p>
Subject Reported Target-lesion Numeric Rating Scale Evaluation by Subjects With Plaque Psoriasis Treated With Clobex® (Clobetasol Propionate) Spray 0.05%
ClinicalTrials.gov study NCT01893567. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Testing Numeric Formats for Presenting Data About Treatment Benefits and Harms
ClinicalTrials.gov study NCT00950014. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Data from: Numerical simulations of targeted delivery of magnetic drug aerosols in the human upper and central respiratory system: a validation study
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Data from: Numerical and experimental study of pyrophoric activated metal Mg surface combustion characteristics
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Data from: Experimental and numerical studies on combustion characteristics of N2-diluted CH4 and O2 diffusion combustion in a packed bed
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Data from: Impact of numerical information on ‘risk knowledge’ regarding human papillomavirus (HPV) vaccination among schoolgirls: a randomised controlled trial
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Data from: Molecular mapping and candidate gene analysis for numerous spines on the fruit of cucumber
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Data from: Numerical simulation of induced cutting in deep coal
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Data from: Numerical cognition in honeybees enables addition and subtraction
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Data from: A multifactor coupling prediction model for the failure depth of floor rocks in fully mechanized caving mining: a numerical and in situ study
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Lethal interactions among forest‐grouse predators are numerous, motivated by hunger and carcasses, and their impacts determined by the demographic value of the victims
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Numerical Simulations of Laboratory-Scale, Hypervelocity-Impact Experiments for Asteroid-Deflection Code Validation
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Data from: Sensory processing by motoneurons: a numerical model for low-level flight control in flies
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Data from: Predicting evolutionary potential: a numerical test of evolvability measures
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Data from: Numerical simulation of the free surface and water inflow of a slope, considering the nonlinear flow properties of gravel layers: a case study
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Data from: The effect of seepage and weak interlayer on the failure modes of surrounding rock - model tests and numerical analysis
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.