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31 results for “Adjoint”
Investigating the conformal behaviour of SU(2) with one adjoint Dirac flavor --- data release
<p>This dataset collects data and analysis results for non-perturbative lattice field theory calculations investigating the SU(2) gauge theory with one Dirac fermion in the adjoint representation. More detailed information is included in the file README_datapackage.md</p>
Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020 --- Data Files
<p>New data on github:</p> <p>https://github.com/sesterhenn/Corona-DataAssimilation</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>doi://10.5281/zenodo.3732292</p> <p>https://zenodo.org/record/3733244</p>
3D Adjoint tomography model of the Italian lithosphere
<p>The project IMAGINE_IT (PI Dr. Dimitri Komatitsch) received 40 million CPU-hours on the Tier-0 GENCI/TGCC CURIE supercomputer as a winner of the 9<sup>th</sup> PRACE consortium call (2014). </p> <p>The awarded computational resources allowed us to construct a new 3D tomographic model for the Italian lithosphere, <em>Im25</em>,<em> </em>by combining spectral-element three-dimensional wavefield simulations and an adjoint-state method.</p> <p>To obtain the final model <em>Im25, </em>we performed 25 adjoint tomography iterations and two source inversion iterations for the initial wavespeed model and an intermediate one.</p> <p>The 3D model <em>Im25</em> resolves P- and S-wavespeed values in the Italian lithosphere for frequencies up to ~0.1 Hz (i.e., periods down to ~10 s). It provides improved images of the subsurface of the peninsula and its neighborhood, highlighting the complex structure of the Adriatic plate on the east of the Italian coast, the debated plumbing system of Mount Etna volcano, and the distribution of fluids and gas (CO<sub>2</sub>) in correlation with the Italian seismicity. </p> <p>The obtained <em>V<sub>p</sub></em> and <em>V<sub>s</sub></em><sub> </sub>models are in agreement with high resolution models developed by classical tomography studies in Italy at local scale, and the retrieved <em>V<sub>p</sub></em>/<em>V<sub>s</sub></em> values correspond to the interpretations on fluids and gas distribution in the Italian subsurface from studies in various geophysical fields. The <em>V<sub>s</sub></em> <em>Im25</em> model also compares favorably with <em>V<sub>s</sub></em> profiles extracted from other studies that use different techniques and datasets, and focus on specific zones of Italy.</p> <p>The provided dataset contains the values of <em>V</em><sub><em>p</em></sub> and <em>V<sub>s</sub></em> (in m/s) for model <em>Im25</em> at each point of the geometrical grid used to discretize the study volume.</p>
Shape Optimization of Thermoacoustic Systems Using a Two-Dimensional Adjoint Helmholtz Solver
<p>rijke stands for Rijke tube and tswc stands for turbulent swirl combustor.</p> <p>mesh_*.xml contains the mesh in xml format.</p> <p>p_dir_*.xml and p_adj_*.xml contain the direct and adjoint fields in xml format.</p> <p>omega_*.txt contains the eigenvalue.</p> <p>my_dict_*.pickle contains the control points and the shape derivatives as byte streams.</p> <p>The dataset also contains the pvd (ParaView Data) files for the amplitude and phase of the direct and adjoint fields.</p>
SU(2) gauge theory with one and two adjoint fermions towards the continuum limit—data release
<p>This package contains all data generated in preparing the publication <a href="https://arxiv.org/abs/2408.00171">SU(2) gauge theory with one and two adjoint fermions towards the continuum limit</a>. It includes four classes of data:</p> <ol> <li>Raw data, as generated from the measurement code running on HPC, in their native formats (raw_data.zip).</li> <li>Metadata around the analysis of the ensembles, in YAML format (ensembles.yaml).</li> <li>Data obtained by analysing the above data and presented in <a href="https://arxiv.org/abs/2408.00171">arXiv:2408.00171</a>, for specific ensembles, in sqlite3 format (su2.sqlite).</li> <li>The above data in (3), and additional data obtained by further analysing them, in CSV format (ensemble_results.csv and gammastar_results.csv).</li> <li>For convenience, the data in (1) above, repackaged in HDF5 format (package.h5).</li> </ol> <p>Each of these is documented in more detail in the file README.md.</p> <p>Due to their size, raw gauge configurations are not included in this package.</p> <p> </p>
Dataset and code for "Adjoint-aided homogenization for flows through heterogeneous membranes"
<p>This dataset and code support the manuscript 'Adjoint-aided homogenization for flows through heterogeneous membranes' by Kevin Wittkowski, Edouard Boujo, François Gallaire and Giuseppe A. Zampogna, under revision in the <em>Journal of Fluid Mechanics</em>, 2024. The data are Stokes flow simulations around an elliptical solid inclusion with periodic boundary conditions. Direct computations of the average flow quantities for several geometry parameters are compared with shape-sensitivity-based predictions obtained by solving a set of direct and adjoint equations around a circular solid inclusion. Please refer to the manuscript for further details.</p> <p><strong>Contents:</strong></p> <ul> <li><em>comsol_code/Mnn.mph, Mtt.mph:</em> COMSOL Multiphysics 6.0 codes used to generate the datasets;</li> <li><em>README.txt</em>: a .txt file describing the code usage;</li> <li><em>Mnn.txt,Mtt.txt</em>: dataset of the direct evaluations of these two average microscopic quantities, input of the .m files;</li> <li><em>S1_Mnn.txt, S2_Mnn.txt, S1_Mtt.txt, S2_Mtt.txt</em>: dataset containing the first and second order shape sensitivities computed on the reference circular geometry, input of the .m files;</li> <li><em>Mnn.m,Mtt.m:</em> MatLab code used to compare the direct evaluations and adjoint predictions of the microscopic quantities;</li> <li><em>ellipse.m</em>: Matlab function to generate the elliptical deformation;</li> <li><em>.fig</em> files: Matlab figures, output of the .m files. They are used to compare the direct evaluation with the prediction based on the adjoint computations.</li> </ul>
Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"
<p>This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023). MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia. The MESWA 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> </p> <p>Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format. Lastly, we include a list of all receivers used in the creation and validation of MESWA. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p> </p> <p>The following table provides a listing of the files in the dataset:</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>MESWA.nc</p> </td> <td> <p>MESWA model in NetCDF format</p> </td> </tr> <tr> <td> <p>MESWA.h5</p> </td> <td> <p>MESWA model in HDF5 format, used by Salvus</p> </td> </tr> <tr> <td> <p>MESWA.xmdf</p> </td> <td> <p>Auxiliary file for MESWA.h5, used to import model into Paraview</p> </td> </tr> <tr> <td> <p>events_project.csv</p> </td> <td> <p>Table of event source parameters for all 327 events considered in the project</p> </td> </tr> <tr> <td> <p>inversion_events_192.csv</p> </td> <td> <p>Table of 192 inversion events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>validation_events_66.csv</p> </td> <td> <p>Table of 66 validation events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_inversion.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_validation.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value)</p> </td> </tr> </tbody> </table> <p> </p> <p> </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, <em>Geophys. J. Int.</em>, 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p> </p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. <em>The Visualization Handbook</em>, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a></p> <p> </p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. <em>Journal of Open Research Software</em>, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a></p> <p> </p> <p>Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402</p> <p> </p>
Repository for adjoint convolution analysis of sea level variations near Charleston and Nantucket
<p>This repository contains adjoint sensitivity, forcing, and example adjoint-convolution script to reconstruct sea level variations near Charleston and Nantucket. </p>
Dataset for "WUS256: An Adjoint Waveform Tomography Model of the Crust and Upper Mantle of the Western United States for Improved Waveform Simulations"
<p>This dataset contains the WUS256 seismic model and auxiliary data used in the creation of the model (Rodgers et al., 2022). WUS256 is a three-dimensional model of the seismic properties of crust and upper mantle of the western United States. The WUS256 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>Also included are the earthquake source parameters for the 72 inversion events and 18 validation events in ASCII text format. Lastly, we include a list of all waveforms used in the creation of WUS256. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p>This effort was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-833624</p>
Dataset for "Imaging of Small-Scale Heterogeneity and Absorption Using Adjoint Envelope Tomography: Results from Laboratory Experiments"
<p>The codes for Monte-Carlo simulation, scripts used to calculate the misfit kernels, and the processed data of the laboratory experiment.</p>
Final data of the adjoint-state full waveform tsunami source inversion, applied to Chile-Iquique tsunami event
<p>We develop an adjoint-state full waveform inversion procedure to recover the initial water elevation of a tsunami event. Traditional finite-fault tsunami source inversion methods suffer from the uncertainty of fault parameters or crustal rigidity. Moreover, the heavy computational burden of calculating Green’s functions results in limited spatial resolution and hinders the real-time applicability of the traditional methods to tsunami early warning. In this work, we apply the adjoint-state full waveform inversion method to the tsunami source inversion. The benefits of the adjoint inversion are two folds: 1) independence of fault parameters, and 2) high computational efficiency, especially for dense tsunami arrays and high resolution grids. We valid this approach with synthetic tsunami sources, and apply it to the 2014 Chile-Iquique tsunami event. Both synthetic and real-data preliminary results show that the adjoint-state method is of high efficiency and high resolution, outperforming the traditional tsunami source inversions. </p> <p>The data is in three comma-separated ascii files. We shared the three inversion results with different starting models. The source region is 70.3~71.5W, 18.5~21S on uniform grids. The src_TRIstart.txt is the inversion result with TRI image starting model, src_USGS_unistart.txt is the inversion result with USGS uniform slip model (https://earthquake.usgs.gov/earthquakes/eventpage/usc000nzvd/finite-fault). The src_zerostart.txt is the inversion result with zero starting model. The text file has longitude (in degrees), latitude (in degrees) and water elevation (in meters) of each column.</p>
Towards the $\beta$ function of SU(2) with adjoint matter using Pauli–Villars fields—Data release
<p>This release contains all data generated in preparing the poster <a href="../records/13361520">Towards the function of SU(2) with adjoint matter using Pauli–Villars fields</a>, presented at <a href="https://conference.ippp.dur.ac.uk/event/1265/overview">Lattice 2024</a> in Liverpool, and its corresponding proceedings paper. It includes two classes of data, each provided both for the one- and two-flavour theories:</p> <ol> <li>Raw data, as generated from the HMC measurement code running on HPC, in their native formats.</li> <li>Metadata around the analysis of the ensembles, in YAML format.</li> </ol> <p>Due to their size, raw gauge configurations are not included in this release.</p> <p>Further documentation on the directory structure and file formats is provided in the file README.md</p>
Dataset for 'Comparing Adjoint Waveform Tomography Models of California Using Different Starting Models'
<p>This dataset includes the three final models for 'Comparing Adjoint Waveform Tomography Models of California Using Different Starting Models.' Each model is an adjoint waveform tomography model of California and Nevada; the models begin from three separate starting models:</p> <ul> <li>CANV_CS begins with CSEM_NA (Krischer et al., 2018; <a href="https://doi.org/10.1029/2017JB015289">https://doi.org/10.1029/2017JB015289</a>)</li> <li>CANV_SP begins with SPiRaL (Simmons et al., 2021; <a href="https://doi.org/10.1093/gji/ggab277">https://doi.org/10.1093/gji/ggab277</a>)</li> <li>CANV_WUS begins with WUS256 (Rodgers et al., 2022; <a href="https://doi.org/10.1029/2022JB024549">https://doi.org/10.1029/2022JB024549</a>)</li> </ul> <p>The models are provided in both NetCDF format and HDF5 format. Figure generation codes and EventXML files for both the inversion and validation event sets can be found <a href="https://doi.org/10.5281/zenodo.7839070">here</a>.</p> <p> </p> <p><em>This effort was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.</em></p>
Adjoint tomography data for LASSIE array by ambient noise differential adjoint tomography
<p>The LASSIE array shear-wave velocity model from ambient noise differential adjoint tomography. </p><p>If you use this dataset, please cite the article published online: </p><p>Liu, X., Beroza, G.C. & Li, H. Ambient noise differential adjoint tomography reveals fluid-bearing rocks near active faults in Los Angeles. <i>Nat Commun</i> <strong>14</strong>, 6873 (2023). <a href="https://doi.org/10.1038/s41467-023-42536-4">https://doi.org/10.1038/s41467-023-42536-4</a> </p><p>Here are the explanations for each of the five columns of the CSV file:<br>- lat: Latitude is a geographic coordinate <br>- lon: Longitude is a geographic coordinate <br>- depth: Depth from surface of the earth<br>- Vs_init: the initial shear wave velocity from ray theory and layered-cake inversion below each surface location<br>- Vs_adj: the adjoint shear wave velocity tomography result from ambient noise differential adjoint tomography</p><p> </p>
Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada"
<p>This dataset includes the model files for the California-Nevada Adjoint Simulations (CANVAS) adjoint waveform tomography model. The model is provided in NetCDF format. The HDF5 file provided is for importing the model into Salvus (Afanasiev et al., 2019; <a href="mondaic.com">mondaic.com</a>) An EventXML file is also provided with information on the events used to calculate the model.</p> <p> </p> <p>Information on the construction of the model can be found in the accompanying publication:</p> <p>Doody, C., Rodgers, A., Afanasiev, M., Boehm, C., Krischer, L., Chiang, A., & Simmons, N. (2023). CANVAS: An adjoint waveform tomography model of California and Nevada. <em>Journal of Geophysical Research: Solid Earth,</em> 128(12). <a href="https://doi.org/10.1029/2023JB027583">https://doi.org/10.1029/2023JB027583</a></p> <p> </p> <p><em>This effort was support by Lawrence Livermore National Laboratory's Laboratory Directed Research and Development project 20-ERD-008. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-856789</em></p>
4D-Var data assimilation experiment of the Lorenz 96 model using an adjoint model of a neural network surrogate model
<p>These data are the output of the 4D-Var data assimilation experiment of the Lorenz96 model using an adjoint model of a neural network surrogate model.<br> The details are described in Nishizawa (2022).<br> </p>
An Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretization of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "An Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretization of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Data from: Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves
<p>This folder contains data from</p> <p>Shimizu, K. (2024), Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves, Journal of Geophysical Research: Oceans, 129, e2023JC020577. https://doi.org/10.1029/2023JC020577</p> <p>Its contents are briefly described in ReadMe.txt.</p>
Data from: Process-based modelling of nonharmonic internal tides using adjoint, statistical, and stochastic approaches. Part II: adjoint frequency response analysis, stochastic models, and synthesis
<p>Meta data updated after publication.</p> <p> </p>
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