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19 results for “numerical methods”
Dataset for "Numerical methods for the detection of phase defect structures in excitable media"
<p>This archive contains the numerical methods presented in the publication "Numerical methods for the detection of phase defect structures in excitable media" as well as the data sets these methods have been applied on. The Python module for Ithildin (py_ithildin.zip) contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (scripts-pdl-detection.zip). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The second variable for the optical data (optical_20200204114234_v.npy) is a delayed version of the first variable. The other files contain simulation results from several finite differences simulations of the mono-domain model. For details, see our paper.</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271351"><strong>Numerical methods for the detection of phase defect structures in excitable media</strong></a><br> Kabus D, Arno L, Leenknegt L, Panfilov AV, Dierckx H (2022) Numerical methods for the detection of phase defect structures in excitable media. PLOS ONE 17(7): e0271351. <a href="https://doi.org/10.1371/journal.pone.0271351">https://doi.org/10.1371/journal.pone.0271351</a></p>
Data for the paper "Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation"
<p>This deposit contains the data obtained by the method described in [A. Bernou, Y. Liu, Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation, 2024]. The notebooks used to generate them through a suitable Euler scheme can be find at https://github.com/ArmdBrn/McKean_PathDep, along with files containing the estimated errors. <br>The two models considered are:<br>- a modified Ornstein-Uhlenbeck model with path-dependency;<br>- a model of neural masses with intrinsic potentiation leading to path-dependent dynamics. </p>
Supplementary data to *Benchmarking of numerical integration methods for ODE models of biological systems*
<p>This archive contains supplementary data and code for the manuscript <strong>Benchmarking of numerical integration methods for ODE models of biological systems </strong>by<strong> Städter et al. 2020</strong>. It contains</p> <ul> <li>scripts to automatically download and install all required packages and models,</li> <li>scripts to compile the models and to perform the study,</li> <li>value files containing all data underlying the analyses in the manuscript,</li> <li>scripts to generate the manuscript figures.</li> </ul> <p>There is a <strong>README.md </strong>file with further information, in particular on what scripts to execute to reproduce the study.</p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
Numerical study of the one-dimensional Holstein model using the momentum-space hierarchical equations of motion method
<p>Data on the finite-temperature current-current correlation function of the one-dimensional Holstein model. Data are obtained using the newly developed momentum-space hierarchical equations of motion (HEOM) method. Details on the method development, as well as on the model parameters, will be given as a supplementary material to a journal publication that will be deposited on arXiv. Folders Regime* contain temporal evolution of the current-current correlation function (j_j_real_time.txt), diffusion constant (diffusion_constant.txt), diffusion exponent (diffusion_exponent.txt), and the electron's spread (delta_x.txt). They also contain frequency profiles of the Fourier transformed current-current correlation function (j_j_real_frequency.txt) and dynamical mobility (dynamical_mobility.txt).</p>
Supplementary codes and datasets for "Efficient numerical method for reliable upper and lower bounds on homogenized parameters"
<p>This repository supports L. Gaynutdinova, M. Ladecký, A. Nekvinda, I. Pultarová, and J. Zeman, <em>Efficient numerical method for reliable upper and lower bounds on homogenized parameters</em> (first announced as the arXiv preprint <a href="http://arxiv.org/abs/2208.09940">2208.09940</a>).</p> <p>In particular, it contains MATLAB source files for reproducing the results presented in Examples 1 and 2 of the manuscript for discretizations with <span class="math-tex">\(N_1 = N_2 = N_3 = 6, 12, 24\)</span>. For other parameters, the code needs to be modified manually.</p> <p>The most recent version of the codes is available in the <a href="https://gitlab.com/ul_bounds_homog_CTU/3d-fem-homogenized-parameters">GitLab repository</a>.</p>
Supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme"
<p>This is the supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme".</p> <p><a href="https://zenodo.org/api/files/87a1c801-d2f8-4c0c-b408-db56481cf97d/Movie%201_w_theta_v_t.mp4">Movie 1_w_theta_v_t.mp4</a>: Time evolution of vertical profiles of buoyancy production and its decomposition.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%202_scatter_w_theta_v_t.mp4">Movie 2_scatter_w_theta_v_t.mp4</a>: Time evolution of scatter plots of w vs theta_v.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%203_boy_incloud_real_z_t.mp4">Movie 3_boy_incloud_real_z_t.mp4</a>: Time evolution of buoyancy production and cloud fraction, and time series of cloud cover.</p> <p><a href="https://zenodo.org/api/files/2f5a17d9-6cb1-4769-88cd-65fd8efd0225/qr_cross.mp4">qr_cross.mp4</a>: Time evolution of cross section of rain water mixing ratio (q<sub>r</sub>) of SDM and SN14 simulation.</p>
Numerical data set belonging to: 'A Finite Volume Parallel Adaptive Mesh Refinement Method for Solid-Liquid Phase'
<p>This data set corresponds to the paper 'A Finite Volume Parallel Adaptive Mesh Refinement Method for Solid-Liquid Phase Change', submitted to Numerical Heat Transfer, Part A: Applications. The numerical data is included in VTK format (to be read by paraView) for the following cases:</p><p>1) 2D Gallium melting in a rectangular cavity (70x50 elements, 140x100 elements, 280x200 elements, 560x400 elements, 1120x800 elements and adaptive mesh)</p><p>2) 3D Gallium melting in a hexagonal cavity (adaptive mesh)</p><p>3) 2D freeze-plug (both steady-state and melting transient): 110x300 elements, 220x600 elements, 440x1200 elements and adaptive mesh)</p><p>Due to the size of the data-set, the data has been split over 8 tar archives featuring a gzip compression. To unpack the data, run the command: cat paper_<i>amr</i>_<i>data.</i>tar.gz.* | tar xzvf -</p><p> </p>
Supplement to the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme"
<p>This is a supplement to the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme".</p> <p>The time evolution of horizontal distribution of LWP:</p> <ul> <li>SDM_lwp_2d_sdm.mp4: from nine SDM runs with different grid resolutions.</li> <li>SN14_lwp_2d_sn14.mp4: from nine SN14 runs with different grid resolutions.</li> </ul> <p>The time evolution of vertical profiles:</p> <ul> <li>sdm_profile_t.mp4: from nine SDM runs with different grid resolutions.</li> <li>sn14_profile_t.mp4: from nine SN14 runs with different grid resolutions.</li> <li>sdm_incloud_t.mp4: vertical profiles in cloudy areas and cloud holes in SDM runs.</li> <li>sn14_incloud_t.mp4: vertical profiles in cloudy areas and cloud holes in SN14 runs.</li> <li>sdm_50x5_incloud_t.mp4: comparison between original SDM and SDM without sedimentation.</li> </ul>
NuBe-DBBM: Numerical Benchmark for Drive-By Bridge Monitoring methods
<p>This repository contains an extensive dataset of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. In addition, the dataset includes results for different road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field of drive-by bridge monitoring.</p> <p>The dataset is made of a collection of individual files, each containing results and information about single vehicle crossing events. The dataset provides results for different dimensions of the problem, which are: monitoring scenario (DSA, DSB), bridge spans (B09, B015, B21, B27, B33, B39), damage location (DL25, DL50), damage magnitude (DM00, DM020, DM40), vehicle model (V1, V2, V5), road profile (P00, PA1, PA2), and event number (E0001, E0002, …, E0800). In total, the dataset contains 518 400 separate files conveniently categorized into a system of subfolders. Each file contains the simulated responses from a 2D representation of the vehicle-bridge interaction problem in Matlab environment. The files here are in <em>.mat</em> format. Refer to the document <em>ReadMe.pdf</em> for extended explanations about the filing structure and file contents. In addition, an extended description of the dataset and numerical modelling can be found in the associated journal publication listed below.</p> <p>Cantero D, Sarwar Z, Malekjafarian A, Corbally R, Makki Alamdari M, Cheema P, Aggarwal J, Noh HY, Liu J. Numerical benchmark for road bridge damage detection from passing vehicles responses applied to four data-driven methods. Archives of Civil and Mechanical Engineering, Vol. 24, Article number 190, 2024.</p> <p>DOI: <a href="https://doi.org/10.1007/s43452-024-01001-9">https://doi.org/10.1007/s43452-024-01001-9</a></p>
Numerical methods for neurobiological networks - Accompanying Data
<p>Data supporting the following tutorial</p> <p>https://github.com/danieleavitabile/numerical-analysis-mathematical-neuroscience</p>
Mixed Precision Optimization Method acceleration time and numerical simulation results
<p>The stored data are the speedup ratios of the MPFlow method and other acceleration algorithms, as well as the elastic wave response data obtained by numerical simulation using the MPFlow method.</p>
A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil Dataset
<p>This dataset contains the reference solutions used for training the two neural networks in 1-, 2- and 3-D cases for the article:"A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil" by Zeyuan Song and Zheyu Jiang, submitted to the journal Water Resources Research. </p> <p>This dataset which describes the relationship between the pressure head and number of particles used to train two MLPs in D-GRW based solvers consists of three files, i.e., 1-, 2- and 3-D case study. There are two parts, original reference solutions and reference solutions, corresponding to the original solutions generated by coarse mesh solvers and solutions after data augmentation process, respectively.The dataset is generated by GRW based solvers and simulation results (e.g., Celia's finite difference method). Original reference solutions admit GRW proportionality assumption. We initialize the number of particles by multiplying the initial condition and 1E10. </p>
Data for: Experimental and numerical methods to ensure comprehensible and replicable electrical stimulation experiments
<p>Replication data for "Experimental and numerical methods to ensure comprehensible and replicable electrical stimulation experiments".</p>
base data for numerical methods exercise
<p>base data for numerical methods exercise</p>
ENGLISH PHRASEOLOGICAL UNITS WITH NUMERALS AND METHODS OF THEIR TRANSLATION INTO UZBEK.
Open the record for dataset details and reuse information.
Numerical Investigation of Observational Flux Partitioning Methods for Water Vapor and Carbon Dioxide
Open the record for dataset details and reuse information.
Trapped Solitary Waves in a Periodic External Force: A Numerical Investigation using the Whitham Equation and the Sponge Layer Method
<p>Supplementary Videos.</p>
Supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme"
<p>This is the supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme".</p> <p><a href="https://zenodo.org/api/files/87a1c801-d2f8-4c0c-b408-db56481cf97d/Movie%201_w_theta_v_t.mp4">Movie 1_w_theta_v_t.mp4</a>: Time evolution of vertical profiles of buoyancy production and its decomposition.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%202_scatter_w_theta_v_t.mp4">Movie 2_scatter_w_theta_v_t.mp4</a>: Time evolution of scatter plots of w vs theta_v.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%203_boy_incloud_real_z_t.mp4">Movie 3_boy_incloud_real_z_t.mp4</a>: Time evolution of buoyancy production and cloud fraction, and time series of cloud cover.</p> <p> </p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.