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138 results for “emulator”
Graph neural network emulator for modeling of ice dynamics and calving in the Pine Island Glacier, Antarctica
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Pine Island Glacier, Antarctica</p> <ul> <li>ISSM_DGL_PIG2.py: Python file for training GNN models (*single.py: code for single GPU environment)</li> <li>ISSM_CNN_PIG.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results (graphs for GNNs)</li> <li>*.pkl: Datasets of the ISSM transient simulation results (grids for CNNs)</li> </ul>
Can ChatGPT emulate humans in software engineering surveys? - ESEM '24
<p>This is the replication package for the paper "Can ChatGPT emulate humans in software engineering surveys?" submitted to the Vision and Reflections track at ESEM '2024.</p>
A Gaussian process emulator for simulating ice sheet-climate interactions on a multi-million year timescale: CLISEMv1.0 (Video supplement)
<p>This video illustrates the ice sheet evolution during a 3 Myr period for three different emulators as described in the manuscript "A Gaussian process emulator for simulating ice sheet - climate interactions on a multi-million year timescale", submitted to Geoscientific Model Development. The ice sheet is forced by declining carbon dioxide concentrations from 980 to 720 ppmv and orbital parameter variations during the late Eocene (between 38 Ma and 35 Ma).</p>
Data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators"
<p><strong>Overview</strong></p> <p>This is the data archive for paper "<a href="https://doi.org/10.5194/gmd-14-5205-2021">Copula-based synthetic data augmentation for machine-learning emulators</a>". It contains the paper’s data archive with model outputs (see <code>results</code> folder) and the Singularity image for (optionally) re-running experiments.</p> <p>For the Python tool used to generate synthetic data, please refer to <a href="https://github.com/dmey/synthia">Synthia</a>.</p> <p><strong>Requirements</strong></p> <ul> <li><a href="https://sylabs.io/singularity/">Singularity</a> >= 3</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler*</li> <li>Today's high-performance computer (e.g. ~ 32 CPUs @ 2 500 MHz with 64 GB of RAM )</li> </ul> <p>*Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=72:00:00</code>).</p> <p><strong>Usage</strong></p> <p>To reproduce the results from the experiments described in the paper, first fit all copula models to the reduced NWP-SAF dataset with:</p> <pre><code>qsub hpc/fit.sh</code></pre> <p>then, to generate synthetic data, run all machine learning model configurations, and compute the relevant statistics use:</p> <pre><code>qsub hpc/stats.sh qsub hpc/ml_control.sh qsub hpc/ml_synth.sh</code></pre> <p>Finally, to plot all artifacts included in the paper use:</p> <pre><code>qsub hpc/plot.sh</code></pre> <p><strong>Licence</strong></p> <p>Code released under <a href="./LICENSE.txt">MIT license</a>. Data from the reduced NWP-SAF dataset released under <a href="./data/LICENSE.txt">CC BY 4.0</a>.</p>
Neural Network Radiation Emulator (KMA/NIMS), January
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (January)</p> <p> </p>
Neural Network Radiation Emulator (KMA/NIMS), October
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (February)</p>
Neural Network Radiation Emulator (KMA/NIMS), September
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (September)</p>
Neural Network Radiation Emulator (KMA/NIMS), August
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (August)</p>
Neural Network Radiation Emulator (KMA/NIMS), July
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (July)</p>
Neural Network Radiation Emulator (KMA/NIMS), April
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (April)</p>
Neural Network Radiation Emulator (KMA/NIMS), June
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (June)</p>
Neural Network Radiation Emulator (KMA/NIMS), March
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (March)</p>
Neural Network Radiation Emulator (KMA/NIMS), February
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (February)</p>
Training simulations for emulator fitting in study of landslide-generated tsunamis in the Makassar Strait
<p><strong>Training simulations used in the study titled 'Probabilistic landslide tsunami estimation in the Makassar Strait, Indonesia, using statistical emulation'</strong></p> <p>Description:</p> <ul> <li>'dem' directory - Bathymetric/Topographic data required to run simulations</li> <li>'Simulation{1..50}' directories: <ul> <li>eta.mp4 - video animation of wave propagation</li> <li>{1..50}.c and topics.h - code and header file for simulation</li> <li>etamax.dat - XYZ file of maximum surface elevation for simulation</li> <li>gauges directory - .dat files containing timeseries outputs for each gauge</li> </ul> </li> </ul>
The machine learning based statistical emulators of GGCMI phase 2
<p>A statistical emulator with machine learning algorithm to reproduce the response of year-to-year variation of four crop yield to CO<sub>2</sub> (C), temperature (T), water (W) and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2 experiment.</p>
Data sets for scattering emulators in momentum space
<p>Interaction data files used to generate the plots for the paper ''Wave-function-based emulation for nucleon-nucleon scattering in momentum space'' by Garcia, Drischler, Furnstahl, Melendez, and Zhang (DOI: <a href="https://doi.org/10.1103/physrevc.107.054001">10.1103/PhysRevC.107.054001</a>).</p>
IoT Emulated Dataset for ICMP/Ping Normal and Malicious Traffic
<p>These datasets are related to Intrusion Detection System, Computer Network Traffic and IoT.</p> <p>These datasets are generated for the purpose of differentiating <strong>ICMP/Ping normal and malicious traffic </strong>that are generated from an embedded device (IoT). The differentiation analysis is done using machine learning.</p> <p>There are three types of files that depend on each module of our research framework. The data generation sequence is as follows:</p> <p>The <strong>pcap files </strong>(network traffic) are generated first, the device used to generate the data is an ESP-01s. Afterwards, the pcap files are transformed into <strong>log files </strong>using the Zeek tool, the log files are then extracted and placed into <strong>CSV files</strong>.</p> <p>The CSV files are labeled and ready for the Machine Learning process.</p> <p> </p> <p>The publication reference for this work is here : https://doi.org/10.1109/ACCESS.2023.3327061</p> <p>The code link: <a href="../badge/latestdoi/619245496">https://zenodo.org/badge/latestdoi/619245496</a></p> <p><strong>This version of the release (0.2.0) is for ping flood with spoofed IPs, however, the previous version (</strong>0.1.0)<strong> is for static IP</strong></p> <p> </p> <p> </p> <p> </p>
Data for "Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity"
<p>The trained 600-member ensemble convolutional neural networks (CNNs) for seasonal tropical cyclone (TC) activity to allow future studies. Please refer Fu et al. (2023; <em>Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity</em>) for more details.</p>
Emulation of the STEP-HFpEF DM Heart Failure Trial in Healthcare Claims Data
ClinicalTrials.gov study NCT06914102. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mortality of MBL-producing Enterobacteriaceae Bacteremias with the Combined Use of Ceftazidime-avibactam and Aztreonam Vs. Other Active Antibiotics. a Multicenter Target Trial Emulation.
ClinicalTrials.gov study NCT06419296. IPD Sharing: NO. Countries: 1. Publications: 15.
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OpenNeuro
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