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218 results for “Physical model”
A Comprehensive Physical Model for the Contrasting Seismogenic Behaviour of Injection Wells in Western Canada
<p>Earthquake catalog for northern Montey play in northeastern British Columbia during 2017-2018.</p>
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
<p>Training, test datasets of the paper "PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions".</p>
Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM
<p>The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.</p>
Hadron Shower Simulation Data for Generative Models in Fundamental Physics
<p>Data set containing pion calorimeter showers used to train and evaluate our generative models for our Hadrons, Better, Faster, Stronger publication. Complete dataset consist of three hdf5 files:</p> <ul> <li>pion_train_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to train the models.</li> <li>pion_eval_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to evaluate the models.</li> <li>pion_eval_steps20to90.hdf5 contains showers originating form pions with discrete energies ranging form 20 GeV to 90 GeV in steps of 10 GeV. This set was used to evaluate the models.</li> </ul> <p>Each file contains a group called 'hcal_only'. This group has two dataset, 'energy' which contains the energy of the pions in GeV and 'layers' which contains the shower images, projected onto a 48x48x48 grid. For our model training this was reduced to 48x25x25 via slicing. The entries correspond to energy depositions in MeV.</p>
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
<p>This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.</p>
Data for "Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales"
<p>The dataset consists of high resolution (250 m) and low resolution (0.025 degree) climate model outputs in netCDF format. Each file contains data for one variable and one month.</p> <p>Low resolution files follow the naming scheme: montrealC_0025deg_200x200_ERA5_1m_YYYYMM_VAR.nc</p> <p>High resolution files follow the naming scheme: montrealC_250m_324x324_ERA5_TEB_100_noconv_YYYYMM_VAR.nc</p> <p>YYYYMM stands for the year (first 4 digits) and month (last 2 digits).</p> <p>_VAR indicates the variable contained in the file:</p> <ul> <li>_UU700 stands for the east-west component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>_VV700 stands for the north-south component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>When _VAR is omitted, the variable is precipitation at 1-minute temporal resolution</li> </ul>
Porous Media Modeling and Physical Property Analysis
<p>This is a supplementary dataset for the submitted manuscript. The dataset includes CT samples, porous media models, permeability and elasticity result data, and Matlab code to implement the modeling.</p>
Research data for "Indirect learning and physically guided validation of interatomic potential models"
<p>This dataset contains structural data, potential parameter files, and data shown in the plots for the publication "Indirect learning and physically guided validation of interatomic potential models". Details of the contents can be found in README.txt.</p>
Datasets for The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations
<p>The data in this repository contains the information needed to reproduce the core results of the paper, "The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations" submitted to the Journal of Advances in Modeling Earth Systems (JAMES) on 10/3/2022.</p> <p> </p> <p>The "intsmoke*.txt" files are text files with the globally integrated smoke mass above 150 hPa for all simulations in the paper. A header is provided in each file.</p> <p>The "spectra*.mat" files are matlab files that contain fields used to plot the kinetic energy spectra for all simulations. The variable "kes" is the kinetic energy as a function of wavelength, "wvl" are the associated wavelengths of "kes". The variables "hcut" and "lcut" represent the indices of the data "kes" and "wvl" that are used to make the kinetic energy spectra plots in the paper.</p> <p>The "budget*.nc" files contain the budget terms for the relative, vertical vorticity evolution equation at the appropriate date/time. The fields in the netcdf file are self-describing. These budget terms are for the "optimal simulation" described in the paper.</p> <p>The "vort*.nc" file contains the vorticity fields just before the plume forcing starts based on the date/time on the file.</p>
Substorm Dataset (SI Dataset 1 - Substorm Identification With The WINDMI Magnetosphere - Ionosphere Nonlinear Physics Model)
<p>Dataset information on substorm onset times, magnetic local times, magnetic latitudes, geographic latitudes, and longitudes, along with detection techniques and their abbreviations (utilized by authors Forsyth, Frey, Liou, Newell, and Ohtani). Columns denote the presence (1) or absence (0) of substorms detected by each technique. Time differences between substorms are recorded. Additionally, it includes counts of techniques detecting substorm onsets, WINDMI model trigger occurrences based on field-aligned current crossing a critical threshold, Geotail current with median and 70th percentile Ic, where Ic doubles as the threshold for WINDMI substorm detection, and maximum field-aligned current during substorm onsets.</p>
The Aliased Complex Oscillator as a Paradigm for Analog Physical Modeling Sound Synthesis --- Audio Samples
<p>Additional material to the paper with the title: "The Aliased Complex Oscillator as a Paradigm for Analog Physical Modeling Sound Synthesis"</p>
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation: Model Simulations
<p>Welcome to our repository, which features simulations from the Hybrid Hydrological Model with Vegetation (H2MV). This collection includes 11 NetCDF files, representing temporal model simulations on a monthly scale and the static output of maximum soil moisture capacity (also known as plant rooting water storage) derived from a 10-fold cross-validation (CV) setup:</p> <ul> <li><strong>Temporal Simulations</strong>: The files named "fold1.nc" through "fold10.nc" contain the temporal model simulations, aggregated to a monthly scale, from 10 fold cross-validation (CV) setup.</li> <li><strong>Static Output</strong>: The "sm_max.nc" file presents the H2MV's estimation of the maximum soil moisture capacity</li> </ul> <h3>Contents of the Temporal Simulation Files</h3> <p>Each of the "fold" files ("fold1.nc" to "fold10.nc") contains the following variables:</p> <ul> <li><strong>Snow Dynamics</strong> <ul> <li><span><code>snow_acc</code></span>: Snow accumulation (mm/day)</li> <li><span><code>snow_melt</code></span>: Snow melt (mm/day)</li> <li><span><code>swe</code></span>: Snow water equivalent (mm)</li> </ul> </li> <li><strong>Evapotranspiration and its components</strong> <ul> <li><span><code>Ei</code></span>: Interception evaporation (mm/day)</li> <li><span><code>Es</code></span>: Soil evaporation (mm/day)</li> <li><span><code>T</code></span>: Transpiration (mm/day)</li> <li><span><code>ET</code></span>: Evapotranspiration (mm/day)</li> </ul> </li> <li><strong>Recharge</strong> <ul> <li><span><code>r_soil</code></span>: Soil recharge (mm/day)</li> <li><span><code>r_gw</code></span>: Groundwater recharge (mm/day)</li> </ul> </li> <li><strong>Runoff</strong> <ul> <li><span><code>runoff_surface</code></span>: Surface runoff (mm/day)</li> <li><span><code>baseflow</code></span>: Baseflow (mm/day)</li> <li><span><code>runoff_total</code></span>: Total runoff (mm/day)</li> </ul> </li> <li><strong>Water Storages </strong> <ul> <li><span><code>GW</code></span>: Groundwater (mm)</li> <li><span><code>SM</code></span>: Soil moisture (mm)</li> <li><span><code>tws</code></span>: Terrestrial water storage (mm)</li> <li><span><code>tws_anomaly</code></span>: Anomalies of terrestrial water storage (mm)</li> </ul> </li> <li><strong>Vegetation</strong> <ul> <li><span><code>fapar</code></span>: Fraction of absorbed photosynthetically active radiation (-)</li> </ul> </li> </ul> <h3>Contents of the Static Output File</h3> <p>The "sm_max.nc" file contains 10 variables corresponding to the 10 folds of CV, with each variable (e.g., "fold1") referring to the respective fold.</p> <h3>Additional Information</h3> <p>It's important to note that the original model simulations were conducted with a daily temporal resolution, but the data shared here have been aggregated to a monthly scale. We are open to sharing the original daily simulations and additional variables not included in this repository upon request. Please feel free to reach out to us for more information or data requests.</p>
Supporting materials for 'Building quantitative skills with a simplified physical model of coastal storm deposition'
<p>Supporting Materials for Lazarus (2024): "Building quantitative skills with a simplified physical model of coastal storm deposition" (preprint <a href="https://doi.org/10.31223/X56H5B">here</a>).</p> <p>Files include:</p> <ul> <li><strong>DEM_washover_final_demo.tif </strong>– "final" DEM for a bare back-barrier floodplain in a physical laboratory experiment of coastal barrier overwash</li> <li><strong>Lazarus_2024_experimental_washover_exercise_instructions_Zenodo_release.pdf</strong> – step-by-step instructions for a classroom exercise that guides students through using the 'DEM_washover_final_demo' file to digitise, measure, and plot washover deposits with QGIS and Python</li> <li><strong>experiments_plotting_simple.ipynb</strong> – Python notebook for plotting results from classroom exercise</li> <li><strong>Lazarus_2024_washover_exercise_figs.ipynb</strong> – Python notebook for plotting Figs. 3 & 4 in the accompanying manuscript (Lazarus, 2024)</li> <li><strong>GGES2021_S24_data_all_release.csv</strong> – dataset of morphometric measurements presented and discussed in the accompanying manuscript (<a href="https://doi.org/10.31223/X56H5B">Lazarus, 2024</a>)</li> </ul>
Physical model and DEM analysis of pile groups under compressive loading
<p>The datesets are about the physical properties of sand and the micromechanical parameters for the DEM simulation</p>
Electron Physics in 3D Two-Fluid Ten-Moment Modeling of Ganymede's Magnetosphere
<p>The dataset is produced for the manuscript "Electron Physics in 3D Two-Fluid Ten-Moment Modeling of Ganymede's Magnetosphere" accepted by the JGR - Space Physics. The script mirdip.lua was used to perform the simulation by the Gkeyll multi-fluid high-moment code. The simulation produced output files in the HDF5 format following the VizSchema description, both are open-source. The output file can be visualized by free, open-source toolkit ParaView. In the file mirdip_q_10.h5, the state quantities are stored in the 4d array "StructGridField" in the layout of NZ * NY * NX * NCOMP, where NX, NY, and NZ are cell numbers in each direction, and NCOMP = 28 is the number of state quantities in the order of ten electron moment terms, ten ion moment terms, six electromagnetic field terms and two correction potential terms.</p>
Seasonal and Interannual Variability of Areal Extent of the Gulf Hypoxia from a Coupled Physical-Biogeochemical Model: A New Implication for Management Practice
<p>netcdf data and code for JGR manuscript: seasonal and interannual variability of areal extent of the Gulf Hypoxia from a coupled physical-biogeochemical model: A new implication for management practice</p>
Supplementary materials for the paper "Computing with liquid crystal fingers: Models of geometric and logical computation." Physical Review E 84.6 (2011): 061702.
<p>When a voltage is applied across a thin layer of cholesteric liquid crystal, fingers of cholesteric alignment can form and propagate in the layer. In computer simulation, based on experimental laboratory results, we demonstrate that these cholesteric fingers can solve selected problems of computational geometry, logic, and arithmetics. We show that branching fingers approximate a planar Voronoi diagram, and nonbranching fingers produce a convex subdivision of concave polygons. We also provide a detailed blueprint and simulation of a one-bit half-adder functioning on the principles of collision-based computing, where the implementation is via collision of liquid crystal fingers with obstacles and other fingers.</p>
WASHTREET. Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model
<p><strong>WASHTREET - Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model.</strong></p> <p>This dataset contains raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques in an urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). The objective of this work is to obtain an accurate representation of the surface velocity distribution as part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The experimental facility is a 36 m<sup>2</sup> full-scale street section and consists of a rainfall simulator placed over a concrete street surface with two gully pots that drain runoff into an underground pipe system. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>).</p> <p>A detailed description of experimental setup, procedure, postprocessing and results can be consulted in ‘<em>1_TestsDescription.pdf’. </em>4K resolution and 25 fps raw videos from which frames are extracted for the PIV analysis are provided for each experiment performed in separated zip files (named as <em>‘2.</em>(test ID)<em>_RawVideos_</em>(configuration)<em>.zip’</em>). Experiments includes three different steady rainfalls of 30, 50 and 80 mm/h of rain intensity and were recorded with and without added fluorescent traces. Data to orthorectify frames from videos are provided in ‘<em>3_SpatialCalibration.zip</em>’. In addition, 60 seconds of steady conditions are extracted for each test and the frames are processed to obtain velocities from a PIV analysis. ‘<em>4_ProcessedFrames_SteadyFlow.zip’ </em>includes the 1500 rectified and processed frames for each experiment to perform the PIV analysis. Results of runoff velocity distributions are included in ‘<em>5_VelocityResults.zip’</em>.</p> <p>Further details of the rainfall simulator, physical model geometry and more hydraulic and sediment transport results can be consulted in <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, data regarding the use of photogrammetry to obtain the elevation map of this physical model is included in <a href="http://www.doi.org/10.5281/zenodo.3241337">WASHTREET Structure from Motion data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13. <a href="https://doi.org/10.1038/s41597-020-0384-z">https://doi.org/10.1038/s41597-020-0384-z</a></li> <li>Naves, J., García, J. T., Puertas, J., & Anta, J. (2021). Assessing different imaging velocimetry techniques to measure shallow runoff velocities during rain events using an urban drainage physical model. <em>Hydrology and Earth System Sciences</em>, <em>25</em>(2), 885-900. <a href="https://doi.org/10.5194/hess-25-885-2021">https://doi.org/10.5194/hess-25-885-2021</a> </li> </ul>
Model output: CICE experiments with varying floe and wave physics
<p>Model output from CICE experiments with varying floe size distribution and wave physics</p> <p> </p> <p>See manuscript below for further details:</p> <p>Roach, L., C. Bitz, C. Horvat, and S. Dean (2019), Advances in modelling interactions between sea ice and ocean surface waves. Journal of Advances in Modeling Earth Systems (in review)</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.