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677 results for “Inversion”
Optical motion capturing of change of direction motions reconstructed with inverse kinematics and dynamics and optimal control simulation
<p>This is the data belonging to the publication "Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data".</p> <p>This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions with a 3D full-body musculoskeletal model by tracking marker and ground reaction force (GRF) data in optimal control simulations. We recorded in total 30 trials with optical motion capture. Using this data, we compared inverse methods (inverse kinematics and dynamics) to coordinate tracking simulations and marker tracking simulations.</p> <p>Please see the README and the publication for further details.</p>
Supplementary Material for "Joint Inversion Based on Variation of Information - A Crustal Model of Wilkes Land, East Antarctica"
<p>Inversion_Results_Cluster.nc - contains geographical and vertical information of clusters</p> <p>Inversion_Results_RealData_Run1.nc - contains inverted susceptibilities and densities after the first inversion run with high coupling</p> <p>Inversion_Results_RealData_Run2.nc - contains inverted susceptibilities and densities after the second continuous inversion run with low coupling</p> <p>Plot.ipynb - python script for visualization</p>
The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau
<p>The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau</p>
Data for the article "Room-Temperature Antiferromagnetic Resonance and Inverse Spin-Hall Voltage in Canted Antiferromagnets"
<p>Data for the article "Room-Temperature Antiferromagnetic Resonance and Inverse Spin-Hall Voltage in Canted Antiferromagnets"</p> <p>(<a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.187201">https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.187201</a> and <a href="https://arxiv.org/ftp/arxiv/papers/2103/2103.16872.pdf">https://arxiv.org/ftp/arxiv/papers/2103/2103.16872.pdf</a>)</p>
Figure 2. Hierarchical access to the image DB-Access Management in Medical Image Databases Based on New Format and Contents Protection with Inverse Pyramid Decomposition
<p>The structure of the hierarchical access to the image database contents is shown on Fig. 2.</p>
Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net
<p>Data presented in the manuscript titled 'Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net'</p>
MIROC4-ACTM CO2 Inversion flux (2001-2022)
<p>This dataset is prepared for GCP CO2-2023 assessment. </p> <p>Inversion Details are in:</p> <div> <div>Chandra, N., Patra, P. K., Niwa, Y., Ito, A., Iida, Y., Goto, D., Morimoto, S., Kondo, M., Takigawa, M., Hajima, T., and Watanabe, M.: Estimated regional CO<sub>2</sub> flux and uncertainty based on an ensemble of atmospheric CO<sub>2</sub> inversions, Atmos. Chem. Phys., 22, 9215–9243, https://doi.org/10.5194/acp-22-9215-2022, 2022</div> <div> </div> <div>MIROC4-ACTM Details:</div> <div><span>Patra, P. K., Takigawa, M., Watanabe, S., Chandra, N., Ishijima, K., and Yamashita, Y.: Improved Chemical Tracer Simulation by MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM), SOLA, 14, 91–96, <a href="https://doi.org/10.2151/sola.2018-016">https://doi.org/10.2151/sola.2018-016</a>, 2018. </span></div> <div> </div> <div> </div> </div>
Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2
<p>The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO<sub>2</sub> fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite.</p> <p>This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or <a href="https://github.com/greenhousegaslab/geostatistical_inverse_modeling">https://github.com/greenhousegaslab/geostatistical_inverse_modeling</a>). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here.</p> <p>Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the <em>load</em> function or can be read into R using the <em>R.matlab</em> package.</p> <ul> <li><strong>H.tar.gz</strong>: This tar file contains the <strong>H</strong> matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (<a href="https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/">https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/</a>). The <strong>H</strong> matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the <strong>H</strong> matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.</li> <li><strong>Z.mat</strong>: This file contains the synthetic OCO-2 observations used in the case study. </li> <li><strong>areas_us.mat</strong>: This file lists the area of each model grid box used in the case study in units of meters<sup>2</sup>. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO<sub>2</sub> fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. </li> <li><strong>distmat.mat</strong>: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. </li> <li><strong>land_mask.mat</strong>: We only estimate CO<sub>2</sub> fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.</li> <li><strong>H_all_OCO2.mat</strong>: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.</li> <li><strong>Xvar.tar.gz</strong>: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows: <ul> <li>Xvar_e.mat Evaporation</li> <li>Xvar_msdwswrf.mat Mean surface downward short-wave radiation flux</li> <li>Xvar_q.mat Specific humidity</li> <li>Xvar_stl1.mat Soil temperature level 1</li> <li>Xvar_stl3.mat Soil temperature level 3</li> <li>Xvar_swvl1.mat Volumetric soil water layer 1</li> <li>Xvar_t2m.mat 2 metre temperature</li> <li>Xvar_tp.mat Total precipitation</li> <li>Xvar_mer.mat Mean evaporation rate</li> <li>Xvar_pev.mat Potential evaporation</li> <li>Xvar_r.mat Relative humidity</li> <li>Xvar_swvl3.mat Volumetric soil water layer 1</li> <li>Xvar_tcc.mat Total cloud cover</li> </ul> </li> </ul>
Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324
<p><strong>Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324</strong></p> <p> </p> <p>Arthur Rodgers</p> <p><em>Geophysical Monitoring Program, Lawrence Livermore National Laboratory, Livermore CA 94551, USA</em>; and</p> <p><em>Department of Earth Sciences, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland</em></p> <p> </p> <p>rodgers7@llnl.gov</p> <p> </p> <p>10.5281/zenodo.11619519</p> <p> </p> <p><strong>Summary</strong></p> <p>This data set includes the metadata and model for the three-dimensional (3D) seismic Earth model WUS324 (Rodgers et al., 2024). This model describes seismic wavespeeds, density and attenuation for the 3D volume spanning the surface to 400 km depth, latitudes from Mexico to Canada (28 to 52) and longitudes from the Pacific Ocean to the Great Plains (-132 to -100). The metadata tabulates the earthquakes and seismic networks and stations used in the creation and validation of 3D seismic Earth model WUS324.</p> <p> </p> <p>The WUS324 model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). https://doi.org/10.1016/b978-012387582-2/50038-1</p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). https://doi.org/10.5334/jors.148</p> <p>Rodgers, A., C. Doody and A. Fichtner (2024). WUS324: Converged Full Waveform Inversion Improves Waveform Fits While Imaging Crustal and Upper Mantle Structure in the Western United States, manuscript in preparation.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was initiated under Laboratory Directed Research and Development project 20-ERD-008 at Lawrence Livermore National Laboratory (LLNL) and continued with support from the National Nuclear Security Administration Ground-based Nuclear Detonation Detection program. AR is grateful to the Eidgenössische Technische Hochschule, Zürich for support as an Academic Guest and to LLNL for Professional Research and Teaching Leave. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. LLNL-MI-865269.</p> <p> </p> <p> </p> <p>Files contained in this data set.</p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description </strong></p> </td> </tr> <tr> <td> <p>WUS324_all_events_project.csv</p> </td> <td> <p>Table of all 216 events considered during the creation of WUS324. This table includes the origin date and time, location and moment tensor parameters.</p> </td> </tr> <tr> <td> <p>WUS324_all_networks.txt</p> </td> <td> <p>Table of all seismic networks and that contributed to WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_events.txt</p> </td> <td> <p>Table of the 126 event names used in the inversions that created WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the creation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_events.txt</p> </td> <td> <p>Table of the 65 event names used in the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.h5</p> </td> <td> <p>WUS324 model for simulating waveforms with minimum period of 16 seconds in Salvus HDF5 format.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.xdmf</p> </td> <td> <pre>Auxiliary file for WUS324_16sec.h5, used to import model into Paraview.</pre> </td> </tr> <tr> <td> <p>WUS324.nc</p> </td> <td> <pre>WUS324 model in netCDF format following the metadata standards of the Incorporated Research Institutions for Seismology Earth Model Collaboratory</pre> </td> </tr> </tbody> </table>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Reinforcement Learning
<p>Well-trained models and generation results for reinforcement learning part of <a href="https://github.com/ytl0410/Polymer-Generative-Models-Benchmark">ytl0410/Polymer-Generative-Models-Benchmark: Well-trained models and generative outcomes for the paper "Benchmarking Study of Deep Generative Models for Inverse Polymer Design" (github.com)</a></p>
Inverse Design of Metamaterials with Manufacturing-Aware Spectrum-to-Shape Diffusion Models
<p>The dataset includes detailed information on the MIM tri-layer metamaterial structures designed and used for training the DiffMeta framework. Specifically, it contains 60000 data:</p> <p>Structural Data: Detailed geometric patterns and composition parameters of the designed MIM tri-layer metamaterial structures.</p> <p>Spectral Data: Spectral measurements on MIM tri-layer metamaterial structures, including emissivity, reflectivity and transmittance spectra across a range of wavelengths.<br><br>The dataset is meticulously organized to facilitate the replication of our study and support further research in the field of metamaterial design. </p>
Input and output datasets for the testing of joint inversion code(s) and sensitivity analysis.
<p>Dataset associated to the paper: "Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis" by Giraud J., Ogarko V., Pakyuz-Charrier E., Jessell M., Lindsay M., and Martin R. <br><br>This paper is under review for publication in Geophysical Journal International. The complete reference will be updated upon publication.</p><p>This dataset comprises:<br>- petrophysical model (x3)<br>- reference models for density contrast and magnetic susceptibility <br>- density contrast and magnetic susceptibility starting models (x3)<br>- inverted density contrast and magnetic susceptibility models (x3)<br>- three probabilistic geological models (x3)</p>
KGloVe DBpedia inverse predicate frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse predicate frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse predicate object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse object split embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse object split weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse Page Rank split embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse Page Rank split weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse Page Rank embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse Page Rank weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
RDF2Vec DBpedia inverse predicate object frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
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