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146 results for “Numerical modelling”
Repository for: "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model"
<p>Set of numerical experiments supporting the paper entitled "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model" by Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard and Marco Toffolon. Submitted to GMD. </p> <p>The folder contains: </p> <p>simulations: DYNO-PODS + Delft3D experiments on Lake Morat. See https://github.com/louisXW/DYNO-pods for more insights on DYNO-PODS and instructions for installation.</p> <p>scripts: extraction and plotting scripts</p> <p>source_code: modified Delft3D src as available at: https://github.com/eawag-surface-waters-research/Delft3D/tree/d3d4/research/surface_heat_transfer</p>
Experimental and numerical study of the effect of model geometric distortion on laboratory modelling of urban flooding
<p>The supporting datasets includes: </p> <p>(1) All the figures in the manuscript in .fig format (in case possible)</p> <p>- Figures in main text</p> <p>- Figure in Support Information</p> <p>(2) Datasets for generating the main outcomes of the manuscript</p> <p>- two files that explain detailed data content in three structure</p> <p>- 3 sub-repositories which contains the results for the three models</p> <p>- one repository contains the data for Figure 8</p> <p> </p>
Numerical modeling data
<p>This dataset contains the numerical modelling results of Models 1-13 showed in the paper "Two Phases of Crustal Shortening in Northeastern Tibet as a Result of a Stronger Qaidam Lithosphere During the Cenozoic India–Asia Collision".</p>
Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes
<p><strong>To watch it in Youtube:</strong></p> <p><a href="https://youtu.be/Uq2qtb4_K2U">https://youtu.be/Uq2qtb4_K2U</a></p> <p><strong>This is a pre-print, please access and cite the published version:</strong></p> <p><a href="https://doi.org/10.1145/3503229.3547057">https://doi.org/10.1145/3503229.3547057</a></p> <p>Automatic analysis of variability is an important stage of <em>Software Product Line</em> (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.</p> <p>In this paper, we make use of the <em>Quality Variability Model</em> (QVM), based on <em>Category Theory</em> (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE -- the state-of-the-art CT tool.</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 Modeling of Asteroid Impact in 2D Cylindrical Coordinates
<p>Additional material and code for the Bachelor Thesis "Numerical Modeling of Asteroid Impact in 2D Cylindrical Coordinates" </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>
Numerical model and natural river data for the timescale analysis of meandering channel migration
<p>This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers.</p> <p>Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.</p>
Models and post-processing codes for paper "Quantitative stratigraphic analysis in a source-to-sink numerical framework"
<p>This package contains all the files required to reproduce the experiments in the manuscript: <strong>Quantitative stratigraphic analysis in a source-to-sink numerical framework</strong>.</p>
A generalized numerical model for clonal growth in scleractinian coral colonies
Open the record for dataset details and reuse information.
Numerical model of the Messinian Mediterranean combining hydrological water balance, river erosion, and flexural isostasy: TISC code and input dataset for the Lago-Mare
Open the record for dataset details and reuse information.
Evaluation of large language model chatbot responses to psychotic prompts: numerical ratings of prompt-response pairs
Open the record for dataset details and reuse information.
2D numerical modelling: the case study of the Arno River at Greve junction
<p>files description:</p> <p>- "nuova mesh_barra.2dm" is the input mesh file for BASEMENT;</p> <p>- "veg. surveys.docx" shows the data collected during the vegetation surveys activities.</p>
numerical modeling data
<p>The first release of our numerical modeling data on the eastern Tibet.</p>
Melt Focusing Along Permeability Barriers at Subduction Zones and the Location of Volcanic Arcs: Numerical models
<p>The dataset includes 2-D subduction zone models calculated by Comsol Mutiphysics®, slab geometry, subduction parameters, and the prediction results.</p> <p>Each numerical model solves the thermal structure of 31 subduction zones. The 2-D slab geometry of each subduction zone is obtained from the compilations of global subduction geometries based on earthquake catalogs Slab 1.0 and Slab2 (Hayes et al., 2012; 2018). Each slab geometry is imported in the corresponding Comsol model as a text file format. Below the point where the slab depth data is unavailable, the slab interface is simply defined as a straight line with the same dip to the bottom of the computation domain. The subduction parameters used in the models are available in Table 1.</p> <p>Using the calculated thermal structure at 30 Ma, we approximate the locations of the arc as the apices of 5 isotherms at 100°C interval within 800°C – 1200°C. The predicted arc locations from each isotherm are reported in Table 2 as the horizontal distance from the trench. The actual arc location in each model is defined as the point on the surface where the slab interface reaches the subarc slab depth <em>H</em> in Table 1 and reported as the horizontal distance from the trench in Table 2. The slab water loss depth and rate obtained from van Keken et al. (2011) are presented in Table 2. In case of the maximum temperature above the water loss depth is higher than the experimentally-derived melting condition, 800°C, we report the horizontal distance from the trench. The width of the horizontal distance of slab water loss depth is assumed as the expected melting region. </p>
Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics (Model Data)
<p>Model Data to reproduce plots from article "Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics". README contains information on where to access model and visualization tools.</p>
Data and scripts for the submission "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models"
<p>Dataset and scripts used to generate Figures for "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models", submitted to the <strong><em>Journal of Advances in Modeling Earth Systems</em></strong> (JAMES).</p> <p>Scripts: Python and NCL</p> <p>Data: Netcdf, PNG, Python pickled objects</p>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Urbanization numerical experiment in Shanghai: WRF model outpts
<div>A numerical experiment was conducted using the WRF model to explore the impacts of urbanization and urban growth on convective rainfall in the subtropical city of Shanghai (China). Details on the experiment and results are summarized in the paper by Qi et al. (2024) entitled: "Contradictory influences of urbanization on intense convective rainfall in a subtropical city".</div> <div> </div> <div>The data is stored in NetCDF format and contains rainfall, moisture, temperature, and omega variables for the three scenarios explored in the paper.</div>
RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset consists of output files of the study reported in:</p> <ul> <li>Tom, M., David, C.H., Marlis, K.M., Zimdars, P.A., Bonassies, Q., Wade, J., Cerbelaud, A., Pavelsky T., Huang, T. (In Review), Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary<br></strong><br>This dataset contains the results of RAPID river discharge simulations (January 1980, February 1980) for the Mississippi river basin using the CURRNT framework. The surface and subsurface runoff data were retrieved from NASA GLDAS Phase 2 dataset (Rodell et al., 2004) at a 3-hourly temporal resolution.<strong><br></strong></p> <p><strong>Software</strong></p> <p>The software used to produce the files in this dataset is available at <a href="https://github.com/czarmanu/currnt" target="_blank" rel="noopener">https://github.com/czarmanu/currnt</a>.</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to Mississippi River Basin.</p> <p><strong>Description of files</strong></p> <p>All files below were prepared by Manu Tom, using the software mentioned above.<br><br>1980-01</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for January 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for January 1980 from RAPID corresponding to the downstream point of each reach.</li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for January 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-02</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for February 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-01-31.</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for February 1980 from RAPID corresponding to the downstream point of each reach. </em></li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for February 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-03</p> <ul> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-03.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-02-29.<br><br></em></li> </ul> <p><strong>Other necessary links associated with this dataset:</strong></p> <p>RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RRR: RAPID model pre-processor (David et al., 2019): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS VIC 3H v2.0 outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0</a></p> <p>NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a><br><br><strong>References</strong></p> <p>David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V. (2011), River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>David, C. H., Hobbs, J., Turmon, M., Emery, C., Reager, J. T., Famiglietti, J. (2019). Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network. Geophys. Res. Lett. 46, 8102–8113, <a href="https://doi.org/10.1029/2019GL083342.492">https://doi.org/10.1029/2019GL083342.492</a></p> <p>Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., et al. (2004). The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p>
ScienceDex guides
Understand access before you commit
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.