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146 results for “Numerical model”
Data from: Prediction of three years of annual rain attenuation statistics at Ka-band in French Guiana using the Numerical Weather Prediction model WRF
<p><span>This study highlights the interest in using an Atmospheric Numerical Simulator (ANS) relying on a high-resolution weather forecast model coupled with an ElectroMagnetic Module (EMM) to compute Ka-band rain attenuation statistics in an equatorial region. An optimization of the parametrisation of the Weather Research and Forecasting meteorological model (WRF) is carried out using measurements collected from a propagation experiment carried out by CNES and ONERA near Kourou in French Guiana. Both simulated and experimental annual Complementary Cumulative Distribution Functions (CCDF) of rain attenuation are presented in this dataset.</span></p> <p>More specifically, this dataset includes the statistical distribution from both the WRF-EMM model and from the propagation experiment for the years 2017, 2018, 2020 and the whole three-year period.</p>
Theoretical and numerical comparison of quantum- and classical embedding models for optical spectra
<p>This repository contains the files for the computational study on "Theoretical and numerical comparison of quantum- and classical embedding models for optical spectra".<br> The repository is organized into different folders as described below:</p> <p>====================================================================================================<br> 1_pna<br> This folder contains the configuration structures for p-nitroaniline extracted from the Molecular Dynamics (MD) simulations in *.xyz format that were used for any further calculations.<br> ====================================================================================================<br> 2_pftaa<br> This folder contains the configuration structures for pentameric formyl thiophene acetic acid extracted from the Molecular Dynamics (MD) simulations in *.xyz format that were used for any further calculations.<br> ====================================================================================================</p> <p><br> We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), The Villum Foundation, Young Investigator Program (EDH, grant no. 29412), the Swedish Research Council (EDH, grant no. 2019-04205), and Independent Research Fund Denmark (EDH, grant no. 0252-00002B and grant no. 2064-00002B) for support.<br> </p>
Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution
<p>This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS. </p>
Codes for: A numerical model supports the evolutionary advantage of recombination plasticity in shifting environments
<p><span>Numerous empirical studies have witnessed an increase in meiotic recombination rate in response to physiological stress imposed by unfavorable environmental conditions.</span> <span>Thus, inherited plasticity in recombination rate is hypothesized to be evolutionarily advantageous in changing environments. Previous theoretical models proceeded from the assumption that organisms increase their recombination rate when the environment becomes more stressful and demonstrated the evolutionary advantage of such a form of plasticity. Here, we numerically explore a complementary scenario – when the plastic increase in recombination rate is triggered by the environmental shifts. Specifically, we assume increased recombination </span><span>in individuals developing in a different environment than their parents and optionally, also in offspring of such individuals. </span><span>We show that such shift-inducible recombination is always superior when the optimal constant recombination implies an intermediate rate. Moreover, under certain conditions, plastic recombination may appear beneficial also when the optimal constant recombination is either zero or free. The advantage of plastic recombination was better predicted by the range of the population's mean fitness over the period of environmental fluctuations, compared to the geometric mean fitness. These results hold for both panmixia and partial selfing, with faster dynamics of recombination modifier alleles under selfing. We think that recombination plasticity can be acquired under the control of environmentally responsive mechanisms such as chromatin epigenetics remodeling.</span></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>
Codes for: A numerical model supports the evolutionary advantage of recombination plasticity in shifting environments
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Data from: Prediction of three years of annual rain attenuation statistics at Ka-band in French Guiana using the Numerical Weather Prediction model WRF
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Dataset of numerical model for enhancing stimulated Brillouin scattering in optical fibers
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Supporting dataset for "Numerical insights into the effects of model geometric distortion in laboratory experiments of urban flooding"
<p>The supporting datasets includes:</p> <p>(1) All the Figures in the manuscript in .fig format </p> <p> - Figures in main text</p> <p> - Figures in SI</p> <p>(2) Original matrice data for the results of each run</p> <p> - A file that explains detailed data content in tree structure;</p> <p> - 4 sub-repositories which contain data for models in different scales</p>
Lituya Bay 1958 Tsunami – pre-event bathymetry reconstruction and 3D-numerical modelling utilizing the CFD software Flow-3D
<p>Simulation video, Model code, STL.File of the solid bodies</p>
The dataset of the manuscript "Numerical study of the initial condition and emission on simulating PM2.5 concentrations in Comprehensive Air Quality Model with extensions version 6.1 (CAMx v6.1): Taking Xi'an as example"
<ul> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/bcfile.rar?versionId=4909d094-5877-408e-bd4f-0c969c54e585">bcfile.rar</a>: the clean initial and boundary condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forNov.rar?versionId=0a1e8b66-5157-4819-8c03-20fb7797d8ef">Emis_forNov.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forDec.rar?versionId=d4db00f1-ec1b-4096-973e-6a87133e4eac">Emis_forDec.rar</a>: the emission files in November and December 2016.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/tuvfile.rar?versionId=5dcf0977-e416-466e-9089-bbf0726c788d">tuvfile.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/o3mapfile.rar?versionId=9c25cae9-f00e-4ad3-b4dc-7a721f7f44d7">o3mapfile.rar</a>: the photolysis files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.cp1.rar?versionId=09ded31b-4c42-4e40-a21c-0f18877e9e41">camx.cp[1-5].rar</a>: the results of sensitivity experiments for using clean initial condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1120p1.rar?versionId=45d6e209-8e66-43ed-bc0b-dc8af5521352">camx.r1120p[1-3].rar</a>: the results of sensitivity experiments for R1120.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1124.rar?versionId=c138e436-0416-4701-948d-ce761cf6c5cf">camx.r1124.rar</a>: the results of sensitivity experiments for R1124.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B12.rar?versionId=34485c43-77ac-4001-8a6d-a57b7ff821e3">contnuous_B12.rar</a>: the results of sensitivity experiments for CT12.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B24.rar?versionId=0f325a61-f19c-4bac-b8b4-7e229c332bf9">contnuous_B24.rar</a>: the results of sensitivity experiments for CT24.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/scripts.zip?versionId=b030444c-51a5-4673-b9d1-7e80ec42a3b9">scripts.zip</a>: all scripts covering every data processing action for all the results reported in the paper.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/data.zip?versionId=52917a53-fca7-4a72-ba2f-a2ce7593adc4">data.zip</a>: final data tables used to plot figures and tables.</li> </ul>
The role of storms in sand wave formation: A numerical modeling study
<p>The data is mainly the results from the simulation for manuscript "The role of storms in sand wave formation: A numerical modeling study".</p>
Data Supplement for "A unified numerical model for wetting of soft substrates"
<p>This dataset contains the data for Fig. 3(a) of our publication</p> <p><em>Aland, S. & Mokbel, D.<br> A unified numerical model for wetting of soft substrates<br> (submitted to International Journal for Numerical Methods in Engineering 2020).</em></p> <p>The data are stored in the files "PresentSimulation.csv", <span class="math-tex">\(\)</span>"ReferenceSolution.csv" and "Experiments.csv".</p> <p>We provide a MLX-File (Live Code File Format), which can be called in a MATLAB editor by entering "Fig3a_live".<br> Alternatively, we also provide a M-File that can be used in the same manner, entering "Fig3a" in a MATLAB editor.</p> <p>We used MatlabR2019b.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Numerical model code, input files and output data for publication ``Mixing and Transformation in a Deep Western Boundary Current: a case study''
<p>Contains numerical model data (code, input files, selected output) to supplement publication ``Mixing and Transformation in a Deep Western Boundary current'', by Spingys and co-authors. All umerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations used in the above-referenced paper.</p>
Data from: Numerical models for assessing the risk of leaflet thrombosis post-transcatheter aortic valve-in-valve implantation
<p>Leaflet thrombosis has been suggested as the reason for the reduced leaflet motion in cases of hypoattenuated leaflet thickening of bioprosthetic aortic valves. This work aimed to estimate the risk of leaflet thrombosis in two post-ViV configurations, using five different numerical approaches. Realistic ViV configurations were calculated by modeling the deployments of the latest version of transcatheter aortic valve devices (Medtronic Evolut PRO, Edwards SAPIEN 3) in the surgical Sorin Mitroflow. Computational fluid dynamics simulations of blood flow followed the dry models. Lagrangian and Eulerian measures of near-wall stagnation were implemented by particle and concentration tracking, respectively, to estimate the thrombogenicity and to predict the risk locations. Most of the numerical approaches indicate on a higher leaflet thrombosis risk in the Edwards SAPIEN 3 device because of its intra-annular implantation. The Eulerian approaches estimated high-risk locations in agreement with the WSS separation points. On the other hand, the Lagrangian approaches predicted high-risk locations at the proximal regions of the leaflets matching the low WSS magnitude regions of both TAVI models and reported clinical and experimental data. The proposed methods can help optimizing future designs of transcatheter aortic valves with minimal thrombotic risks.</p>
Fault interaction and its impact on crustal extrusion in southeastern Tibetan Plateau: insights from geodynamical numerical modeling
<p>Files include the data of the numerical model and model results of all cases in the study.</p>
Model codes, data, and plot scripts for the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning"
<p>The model codes, data, and plot scripts used in the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning".</p><ul><li>Mods: the modified CAM model code used in these three experiments.</li><li>Results: post-processing NCL scripts and the results used for making plots.</li><li>figs: the NCL scripts and figures used in the paper.</li><li>Parcel: parcel model that represents the ice nucleation process.</li></ul><p> </p>
Numerical modeling results for "Rift propagation interacting with pre-existing microcontinental blocks"
<p>Opensource software Paraview is required to open the vtr files. </p><p>Opensource software Matlab is required to open the mat and m files. </p>
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization -- Reproducibility Files
<p>This repository contains the files to reproduce the results from the paper "Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization".</p> <p>Data Collection</p> <p>In this folder, all files used to generate the raw performance data for the set of algorithms are included, as well as the code used to generate the ELA features. For the performance, the packages 'ioh', 'nevergrad', 'modde' and 'modcma' are essential, while 'pflacco' is used for ELA. For all scripts in this folder, the number of parallel threads and the folders for reading function settings (included as 3 csv files in this folder) and storing data should be set before execution. </p> <p>Note that the full performance data exceeds 50GB, so it is not included in this repository. Instead, the results of processing it (using the aocc_extraction script) are included in the 'auc_MABBOB' folder (spread across multiple csv-files, with a version using a different budget factor included as well).</p> <p>The ELA data is included as 'ELA' and 'ELA_BBOB' for the affine combinations and component functions respectively. </p> <p>Data Processing, Analysis and Visualization</p> <p>The remaining reproducibility files can be found in the Reproducibility folder. Within this folder are several notebooks which handle various steps in the pipeline, starting with preprocessing the data collected in the previous steps. This results in some csv-files, which are also included for convenience. Afterwards, the remaining notebooks deal with correlation analysis, instance selection methods, and all included plots from the paper. To match the environment used during our execution of these scripts. a yml-file (to be used with conda or mamba) is available as well. </p>
Software file and numerical results of Modelling heat transfer for assessing the convection length in ventilated caves
<p>The Comsol file corresponding to the reference case as shown in Figures 4-6 as well as all the numerical results for the rest of the figures are available here.</p>
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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.