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Dataset results
8 results for “Symbolic Regression”
Datasets for "Machine-Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks"
<p>This collection contains the datasets and associated files used in the research presented in the manuscript titled "Machine Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks". The datasets are critical for the development and validation of machine learning and symbolic regression models aiming to predict methane storage capacities in covalent organic frameworks (COFs).</p> <p><strong>Included Datasets:</strong></p> <ol> <li><code>COF_Data_for_ML.csv</code>: This dataset was utilized for the development of machine learning models.</li> <li><code>COF_Data_for_SISSO.csv</code>: This dataset was employed for the development of SISSO-based symbolic regression models.</li> <li><code>ML_vs_GCMC.xlsx</code>: This comparative dataset features GCMC-calculated results alongside machine learning predictions.</li> <li><code>Feature_Combination.xlsx</code>: This file contains data detailing all the feature combinations explored in the study.</li> <li><code>ML_SISSO_GCMC.xlsx</code>: This comparative dataset includes GCMC calculations, SISSO-based symbolic regression model predictions, and ML predictions.</li> <li><code>Crystallographic_Properties_of_535k_COFs.xlsx</code>: This consolidated dataset presents the crystallographic properties of 535,293 COFs.</li> </ol> <p><strong>Software Used:</strong></p> <ul> <li>Machine Learning Computations: Scikit-Learn (<a href="https://scikit-learn.org/stable/" target="_new">https://scikit-learn.org/stable/</a>)</li> <li>GCMC Simulations: RASPA2 (<a href="https://github.com/iRASPA/RASPA2" target="_new">https://github.com/iRASPA/RASPA2</a>)</li> <li>SISSO Calculations: SISSO toolkit (<a href="https://github.com/rouyang2017/SISSO" target="_new">https://github.com/rouyang2017/SISSO</a>)</li> <li>Crystallographic property calculations: Zeo++ (<a href="https://www.zeoplusplus.org/" target="_new">https://www.zeoplusplus.org/</a>)</li> </ul> <p>The datasets are provided to enable replication of the study's findings, encourage further research in the field, and facilitate the development of advanced predictive models by the scientific community. Researchers who use these datasets are requested to cite this Zenodo entry as well as the associated paper upon its publication.</p>
GPAM: Genetic Programming with Associative Memory - datasets for symbolic regression
<p>This collection contains five data sets for symbolic regression generated using functions known as Koza-1, Nguyen-7, Nguyen-10, Korns-1, and Korns-4. In each function, we replaced some data points with randomly generated values from interval [−10, 10].</p>
Exhaustive Symbolic Regression Function Sets
<p>ESR (Exhaustive Symbolic Regression) is a symbolic regression algorithm which efficiently and systematically finds all possible equations at fixed complexity (defined to be the number of nodes in its tree representation) given a set of basis functions. This is achieved by identifying the unique equations, so that one minimises the number of equations which one would have to fit to data.</p> <p>Here we provide the functions generated, the unique equations, and the mappings between all equations and unique ones using different sets of basis functions. These are:</p> <ul> <li>"core_maths": <span>\(\{x, a, {\rm inv}, +, -, \times, \div, {\rm pow} \}\)</span></li> <li>"ext_maths": <span>\(\{x, a, {\rm inv}, \sqrt{\cdot}, {\rm square}, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> <li><span>"base_e_maths": \(\{x, a, {\rm inv}, \exp, \log, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> </ul> <p>where <span>\(x\)</span> is the input variable and <span>\(a\)</span> denotes a constant.</p> <p>One can fit these functions to a data set of interest by using the <a href="https://esr.readthedocs.io">ESR package</a>.</p>
Results files from the 2022 SRBench Competition: Interpretable Symbolic Regression for Data Science
<p>Results files from the 2022 SRBench Competition: Interpretable Symbolic Regression for Data Science</p>
Data for "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models"
<p>This repository contains all geo-physical catchment properties used in the publication "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models".</p>
Physical deep symbolic regression to learn crack tip correction formulas
<p>This repository publishes the data generated in the article "A universal crack tip correction algorithm discovered by physical deep symbolic regression" (see preprint: <a title="arXiv.2403.10320" href="https://doi.org/10.48550/arXiv.2403.10320" target="_blank" rel="noopener">arXiv.2403.10320</a>).</p> <p>This repository is structured with the following subfolders:</p> <ul> <li><strong><em>01_Simulation_Output</em></strong>: The results of the finite element (FE) simulations described in the paper</li> <li><strong><em>02_CrackPy_single_evaluation</em></strong>: For each FE simulation, the fracture analysis results of <a href="https://github.com/dlr-wf/crackpy" target="_blank" rel="noopener">CrackPy</a> performed with the crack tip as origin</li> <li><strong><em>04_CrackPy_random_evaluation_pipeline</em></strong>: For each FE simulation, the fracture analysis is performed at 1000 random perturbations of the crack tip position as origin and the results are stored in the subfolder <em>samples</em></li> <li><strong><em>05_1_PhySO_log_mode_I</em>,<em> 05_2_PhySO_log_mode_II, 05_3_PhySO_log_mixed_mode</em></strong>: These folders contain the logs of three distinct training runs of <a href="https://github.com/WassimTenachi/PhySO" target="_blank" rel="noopener">PhySO</a> - mode I, mode II, and mixed mode. For each of these three load cases, we train symbolic regression models for the x-correction and y-correction separately. The symbolic regression results are stored in the files <em>curves_pareto.csv </em></li> <li><strong><em>06_Pareto_Plots</em></strong>: The visualization of the Pareto front for each training run of PhySO</li> <li><strong><em>07_Plots_vector_fields</em></strong>: The correction vector fields for each discovered Pareto formula</li> <li><strong><em>08_Convergence_study_FEA</em></strong>: The iterative convergence behavior for the FE simulations data</li> <li><strong><em>13_Application</em></strong>: We applied the most promising formulas to experimental DIC data from uniaxial and biaxial fatigue crack growth experiments. The results are contained in this folder.</li> </ul> <p>The code to reproduce these results can be fould on our GitHub page at the following link: <a href="https://github.com/dlr-wf/crack_tip_correction_symbolic_regression" target="_blank" rel="noopener">https://github.com/dlr-wf/crack_tip_correction_symbolic_regression</a></p>
Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression
<p>Dataset associated with the publication "Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression" by Sachin Jog, Daniel Vázquez, Lucas F. Santos, José A. Caballero, and Gonzalo Guillén-Gosálbez. The dataset includes the parameters used in the DACE BB models of case studies 1 and 2, described in sections 5.1.3 and 5.2.3 of the supplementary material, respectively.</p>
Symbolic Regression Model To Predict Choledocholithiasis
ClinicalTrials.gov study NCT04410848. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.