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62 results for “Solver”
Data for the paper "PETScML: Second-Order Solvers for Training Regression Problems in Scientific Machine Learning"
Open the record for dataset details and reuse information.
CAABA/MECCA model output for evaluating optimized step size control in Rosenbrock solvers for stiff ODEs
<p>This dataset comprehend the simulation output obtained with the CAABA/MECCA model for the optimization of the step size control in the Rosenbrock integrators available from the Kinetic PreProcessor (KPP) version 2.2.3_rs4. This dataset is made avalaible for the peer-review of the manuscript describing the model and the different options of step size control that was implemented. </p>
Comparison Results for Global Optimization Solvers Using a MIMO System With Two, Three and Four Active Layers as a Benchmark
<p>Benchmark data for GPU-based interval optimization of a simulated MIMO system with two, three and four active layers. We compare popular optimization tools from C-XSC and GNU Octave to purely brute-force based techniques for the GPU. Execution times were measured in seconds.</p>
Semi-implicit barotropic mode solver using ForTrilinos in MPAS-O and its test data
<p>To run the ForTrilinos-enabled MPAS-O,</p> <ol> <li>Download all files</li> <li>Install Trilinos <ul> <li>Unzip: tar -xzvf Trilinos.tar.gz</li> <li>cd Trilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./</li> <li>Check install directories and options in 'do-configure_gnu'</li> <li>Run 'do-configure_gnu'</li> <li>Trilinos information & installation refer to <a href="https://trilinos.github.io/">https://trilinos.github.io/</a></li> </ul> </li> <li>Install ForTrilinos (inside Trilinos) <ul> <li>cd Trilinos/ForTrilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./</li> <li>Check Trilinos and ForTrilinos install directories and options in 'do-configure_gnu'</li> <li>Run 'do-configure_gnu'</li> <li>ForTrilinos information & installation refer to <a href="https://fortrilinos.readthedocs.io/en/latest/">https://fortrilinos.readthedocs.io/en/latest/</a></li> </ul> </li> <li>Install ForTrilinos-enabled MPAS-O <ul> <li>Unzip: tar xzvf MPAS-Model_fortrilinos.tar.gz</li> <li>cd MPAS-Model_fortrilinos</li> <li>Check ForTrilinos directories at line 478 (FORTRILINOS_ROOT) in 'Makefile' </li> <li>Install PIO (refer to <a href="https://ncar.github.io/ParallelIO/">https://ncar.github.io/ParallelIO/</a>)</li> <li>MAPS-O information & installation refer to <a href="https://mpas-dev.github.io/ocean/ocean.html">https://mpas-dev.github.io/ocean/ocean.html</a></li> <li>For GNU compiler: make gnu-nersc USE_PIO2=true FORTRILINOS=true</li> </ul> </li> <li>Run test cases <ul> <li>Example <ul> <li>cd MPAS-O_Initial_data/baroclinicEddies/strong_scaling</li> </ul> </li> <li>Link a MPAS-O compiled executable to a test case directory: <ul> <li>ln -fs MPAS-Model_fortrilinos/ocean_model MPAS-O_Initial_data/baroclinicEddies/strong_scaling/</li> </ul> </li> <li>Link a XML deck (solver configurations) for Trilinos to a test case directory <ul> <li>ln -fs xml_decks/no_precond/stratimikos.xml_SCG MPAS-O_Initial_data/baroclinicEddies/strong_scaling/stratimikos.xml</li> </ul> </li> <li>Run <ul> <li>mpirun -n $N ocean_model</li> <li> <p>If the simulation was successful, you will see:</p> <pre><code>tail -n 1 log.ocean.0000.out Logging complete. Closing file at ...</code></pre> <p> </p> </li> </ul> </li> <li>For the global test case, please download here: <a href="https://doi.org/10.5281/zenodo.1252425">MPAS-O_V6.0_RRS30to10.tar</a>. Please see <a href="http://mpas-dev.github.io/">http://mpas-dev.github.io</a> for User's Guide, github release page, description of each test case, and more.</li> </ul> </li> </ol>
Dataset of ``Plasma Distribution Solver: A Model for Field-Aligned Plasma Profiles Based on Spatial Variation of Velocity Distribution Functions"
<p>This dataset contains the plasma distribution data in the Jupiter–Io system, calculated from the Plasma Distribution Solver and used for figures in the paper “Plasma Distribution Solver: A model for field-aligned plasma profiles based on spatial variation of velocity distribution functions” by K. Saito et al. (2023).</p> <p> </p> <p>The contents of files ‘all_Case_1.csv’ and ‘all_Case_2.csv’ are as follows:</p> <ul> <li>Position along the magnetic field line (0 at the magnetic equator) [m] (column 1)</li> <li>Distance from the Jovian center [km] (column 2)</li> <li>Magnetic latitude [rad]([degree]) (column 3(4))</li> <li>Magnetic flux density [T] (column 5)</li> <li>The initial condition of electrostatic potential [V] (column 6)</li> <li>The result of electrostatic potential [V] (column 7)</li> <li>Number density profiles [m<sup>-3</sup>] (columns 8-17)</li> <li>Charge density profiles obtained from the integration of velocity distribution functions [C m<sup>-3</sup>] (column 18)</li> <li>Charge density profiles obtained from Poisson’s equation [C m<sup>-3</sup>] (column 19)</li> <li>Convergence value (column 20)</li> <li>Particle flux density [m<sup>-2</sup> s<sup>-1</sup>] (columns 21-30)</li> <li>Mean flow velocity parallel to the field line [m s<sup>-1</sup>] (columns 31-40)</li> <li>Plasma pressure perpendicular to the field line [Pa] (columns 41-50)</li> <li>Plasma pressure parallel to the field line [Pa] (columns 51-60)</li> <li>Plasma dynamic pressure [Pa] (columns 61-70)</li> <li>Perpendicular temperature [J] (columns 71-80)</li> <li>Parallel temperature [J] (columns 81-90)</li> <li>Alfvén speed considering the displacement current term in Ampère’s law [m s<sup>-1</sup>] (column 91)</li> <li>Alfvén speed per the speed of light (column 92)</li> <li>Ion inertial length using averaged mass [m] (column 93)</li> <li>Electron inertial length [m] (column 94)</li> <li>Ion Larmor radius using averaged mass [m] (column 95)</li> <li>Ion acoustic gyroradius using averaged mass [m] (column 96)</li> <li>Electron Larmor radius [m] (column 97)</li> <li>Current density [A m<sup>-2</sup>] (column 98)</li> </ul> <p>The Python codes ‘plot_all.py,’ ‘plot_plasma_beta_comparison.py,’ and ‘plot_Alfven_speed_comparison.py’ can plot Figures 5, 6, 7, and 9 of the paper using the above CSV files.</p> <p> </p> <p>The files ‘boundary_conditions_Case_1.csv’ and ‘boundary_conditions_Case_2.csv’ contain the boundary conditions for Cases 1 and 2.</p> <p> </p> <p>The zip files ‘probability_density_function_Case_1_H_Io.zip’ and ‘probability_density_function_Case_1_H_Jupiter_North.zip’ are zipped CSV files with the same name. The contents of these files are as follows:</p> <ul> <li>Magnetic latitude [degree] (column 1)</li> <li>Perpendicular velocity at the particle position [m s<sup>-1</sup>] (column 2)</li> <li>Parallel velocity at the particle position [m s<sup>-1</sup>] (column 3)</li> <li>Perpendicular velocity at the boundary [m s<sup>-1</sup>] (column 4)</li> <li>Parallel velocity at the boundary [m s<sup>-1</sup>] (column 5)</li> <li>Probability density function [s<sup>3</sup> m<sup>-3</sup>] (column 6)</li> <li>Differential flux per number density [cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> keV<sup>-1</sup>] (column 7)</li> </ul> <p>The Python code ‘plot_velocity_distribution_function.py’ can plot Figure 8 of the paper using this CSV file.</p>
Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms
<p>In accordance with EPSRC funding requirements this folder contains all raw data relevant to the named paper: </p> <p>Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms</p> <p><br> Journal: Annals of Nuclear Energy</p>
Model Counting Competition 2020: Submitted Solvers
<p>The dataset contains the submissions that have been evaluated in the Model Counting Competition 2020 on the tracks:</p><p>- Track 1 (Model Counting)<br>- Track 2 (Weighted Model Counting)<br>- Track 3 (Projected Model Counting)</p><p><br>Details will be made public in the publication "The Model Counting Competition 2020" by Fichte, Hecher, and Hamiti. Due to numerous names, we omit authors of the solvers from the meta information of this dataset. We refer to the aforementioned report for details.</p><p>We also refer to the competition website: https://mccompetition.org/</p><p> </p><p>Changelist:</p><p>2023-10-21 (v2): We updated wrappers to take the more recent competition format (MC2021 format), which is DIMACS CNF compatible.</p>
A CMOS-compatible oscillation-based VO2 Ising machine solver: Waveforms and code
Open the record for dataset details and reuse information.
Model Counting Competition 2024: Submitted Solvers
<p>The dataset contains the submissions that have been evaluated in the Model Counting Competition 2024 on the tracks:</p> <ul> <li>Track 1 (Model Counting)</li> <li>Track 2 (Weighted Model Counting)</li> <li>Track 2b (Weighted Model Counting Bonus Track)</li> <li>Track 3 (Projected Model Counting)</li> <li>Track 4 (Projected Weighted Model Counting)</li> </ul> <p><br>Details will be made public in an upcoming report.</p> <p>We also refer to the competition website: https://mccompetition.org/</p>
Benchmarking Different QP Formulations and Solvers for Dynamic Quadrupedal Walking
Open the record for dataset details and reuse information.
LISFLOOD-FP 8.1: New GPU accelerated solvers for faster fluvial/pluvial flood simulations - video supplement
<p>These are video supplement files to Sharifian et al. (2022), to give step-by-step instructions on how to download and install LISFLOOD-FP8.2, and reproduce the simulations for representative case studies. For a full description of the methodology and case studies, please refer to the manuscript.</p>
LISFLOOD-FP 8.1: New GPU accelerated solvers for faster fluvial/pluvial flood simulations - simulation results
<p>Simulation result data for Sharifian et al. (2022) for the following case studies:</p> <p>1- Lower Triangle catchment</p> <p>2- Upper Lee catchment</p> <p>3- Eden catchment</p> <p>4- Glasgow urban area</p> <p>5- Cockermouth urban area</p>
Files for "Ocellaris: a discontinuous Galerkin finite element solver for two-phase flows with high density differences"
<p>Input files and scripts sufficient to reproduce the results shown in the paper "Ocellaris: a discontinuous Galerkin finite element solver for high density ratio two phase flows".</p> <p>A version of Ocellaris similar to version 2019.0.1 was used and all results should be reproducible by this version of Ocellaris. See <a href="https://bitbucket.org/ocellarisproject/ocellaris/">the Ocellaris source code repository</a>, <a href="https://pypi.org/project/ocellaris/2019.0.1/">Ocellaris 2019.0.1 on PyPi</a>, or DOI <a href="https://zenodo.org/record/2558303">10.5281/zenodo.2558303</a>.</p> <p>The Ocellaris user guide can be found at <a href="https://www.ocellaris.org">www.ocellaris.org</a> and it includes guides to how to run Ocellaris in Singularity or Docker containers for ease of installation and testing. Containers for Ocellaris 2019.0.0 are available, for Docker run:</p> <pre><code class="language-bash"># On the host machine: docker run -it trlandet/fenics-dev:2018.1.0.r3 # Inside the Docker container pip3 install ocellaris==2019.0.1 --user </code></pre> <p> </p>
Input Files and Processed Results for FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows
<p><strong>Input Files (StartingFiles.zip) and Processed Results (FreeMHDPaperAllFigures.zip) used to make paper figures. </strong></p> <p> </p> <p>FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows</p> <p><em>The extreme heat fluxes in the divertor region of tokamaks may require an alternative to solid plasma-facing components, for the extraction of heat and the protection of the surrounding walls. Flowing liquid metals are proposed as an alternative, but raise additional challenges that require investigation and numerical simulations. Free surface designs are desirable for plasma-facing components (PFCs), but steady flow profiles and surface stability must be ensured to limit undesirable interactions with the plasma. Previous studies have mainly used steady-state, 2D, or simplified models for internal flows and have not been able to adequately model free-surface liquid metal (LM) experiments. Therefore, FreeMHD has been recently developed as an open-source magnetohydrodynamics (MHD) solver for free-surface electrically conductive flows subject to a strong external magnetic field. The FreeMHD solver computes incompressible free-surface flows with multi-region coupling for the investigation of MHD phenomena involving fluid and solid domains. The model utilizes the finite-volume OpenFOAM framework under the low magnetic Reynolds number approximation. FreeMHD is validated using analytical solutions for the velocity profiles of closed channel flows with various Hartmann numbers and wall conductance ratios. Next, experimental measurements are then used to verify FreeMHD, through a series of cases involving dam breaking, 3D magnetic fields, and free-surface LM flows. These results demonstrate that FreeMHD is a reliable tool for the design of LM systems under free surface conditions at the reactor scale. Furthermore, it is flexible, computationally inexpensive, and can be used to solve fully 3D transient MHD flows.</em></p>
CFF SAT-solver dataset
<p>Dataset created by using CFF SAT-solver created as a part of Master's Thesis.</p>
UBER v1.0: A universal kinetic equation solver for radiation belts
<p>The zip file contains all scripts and simulation data used for producing Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Table 2 of the manuscript "UBER v1.0: A universal kinetic equation solver for radiation belts".</p>
dataset Master Thesis Optimising parity game solvers using dynamic SCC maintenance
<p>Complete dataset of my Master Thesis Optimising parity game solvers using dynamic SCC maintenance</p>
Artifacts for Fuzzing SMT Solvers with Diversified Sub-formulas
<p><strong>Fuzzing SMT solvers with Diversified Sub-formulas</strong></p> <p>Table of Contents</p> <ul> <li>Background</li> <li>Install</li> <li>Usage</li> <li>Bugs</li> </ul> <p> </p> <p><strong>Background</strong></p> <p><strong>Octopus </strong>is the tool for detecting soundness bugs in SMT solvers. <br> We have submitted 10 valid bug reports for Z3 so far. 7 bugs are confirmed/fixed by developers among these reports.</p> <p> </p> <p><strong>Install</strong></p> <p>Octopus itself has few dependencies. It uses Python3 and Python-virtualenv.</p> <p>You can install Python-virtualenv using <code>pip install virtualenv</code></p> <p>Then install Octopus.</p> <p><code>virtualenv --python=/usr/bin/python3.6 virenv</code></p> <p><code>source virenv/bin/activate</code></p> <p><code>cd octopus</code></p> <p><code>python3 setup.py install</code> </p> <p> </p> <p><strong>Usage</strong></p> <p>You can download SMT instances in SMT COMP 2021 as benchmarks.</p> <p><a href="https://www.starexec.org/starexec/secure/explore/spaces.jsp?id=1">2021-05-26 - StarExec</a></p> <p>Then install and build the SMT solver you want to test.</p> <p>For example:</p> <p><code>git clone https://github.com/Z3Prover/z3.git</code></p> <p><code>python scripts/mk_make.py </code></p> <p><code>cd build; make</code></p> <p>Then Octopus can be used to validate it, for example:</p> <p><code>octopus --benchmark=/home/SMT2021 --solver=z3 --solverbin=../z3/build/z3 --theory=LIA</code></p> <p>To run Octopus in multiple cores:</p> <p><code>octopus --benchmark=/home/SMT2021 --solver=z3 --solverbin=../z3/build/z3 --theory=LIA --cores=20</code></p> <p> </p> <p><strong>Bugs</strong></p> <p>Octopus has detected many new refutational soundness bugs in Z3.</p> <p>Here is a list of issues we reported.</p> <p><a href="https://github.com/Z3Prover/z3/issues/5373">https://github.com/Z3Prover/z3/issues/5373</a> [confirmed]<br> <a href="https://github.com/Z3Prover/z3/issues/5443">https://github.com/Z3Prover/z3/issues/5443</a> [reported]<br> <a href="https://github.com/Z3Prover/z3/issues/5447">https://github.com/Z3Prover/z3/issues/5447</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5456">https://github.com/Z3Prover/z3/issues/5456</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5457">https://github.com/Z3Prover/z3/issues/5457</a> [fixed] <br> <a href="https://github.com/Z3Prover/z3/issues/5460">https://github.com/Z3Prover/z3/issues/5460</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5468">https://github.com/Z3Prover/z3/issues/5468</a> [fixed] <br> <a href="https://github.com/Z3Prover/z3/issues/5488">https://github.com/Z3Prover/z3/issues/5488</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5502">https://github.com/Z3Prover/z3/issues/5502</a> [duplicate]<br> <a href="https://github.com/Z3Prover/z3/issues/5508">https://github.com/Z3Prover/z3/issues/5508</a> [reported]<br> <a href="https://github.com/Z3Prover/z3/issues/5423">https://github.com/Z3Prover/z3/issues/5423</a> [invalid] </p>
Data for "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks"
<p>This data is related to the manuscript "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks". It contains meshes, boundary conditions, and medium properties for discrete fracture networks used to test fluid flow solvers.</p>
Traveltime calculations for qP, qSV and qSH waves in 3D tilted transversely isotropic media using fast sweeping method with a Newton method local solver
<p>Data for the 3D BP model, which is used in the numerical example test for fast sweeping method </p>
ScienceDex guides
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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.