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31 results for “Bayesian Optimization”
AiNU data for Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
<p>This file contains the AiNU data used for the article entitled by <em>Physics-based material parameters extraction from perovskite experiments via Bayesian optimization</em> (https://arxiv.org/abs/2402.11101).</p>
Electronic Structure Data for "Design of Covalent Organic Frameworks through on-the-fly Batch-based Bayesian Optimization"
<p>This is a dataset of 1736 potential building blocks for the construction of covalent organic frameworks (COF). Electronic structures were calculated with the GFN1-xTB tight binding DFT approach as implemented in the xTB package (v6.2.3). The dataset contains all necessary inputs and outputs from these calculations. Structures were optimised with xTB's internal normal coordinate rational function optimizer (ANCopt) at the default geometry convergence criterion.</p> <p>The dataset contains calculations for two major parameters determining the suitability of the resulting COFs as an organic semiconductor, specifically, the approximate energy alignment of the homo level and the reorganization free energy.</p>
Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes
<p>Yield and Conversion measurements for iodoalkylation reaction of four different terminal alkynes. The reaction conditions as well as the equivalent of the reactants and reagents for each of the three optimizers are listed in the corresponding JSON file. </p>
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies
<p>This repository contains the nexus files and MrBayes command files needed for running the experiments to determine the optimal word list size required for inferring the best phylogenies.</p> <p>The paper is forthcoming at <strong>The 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, New-Mexico, USA</strong>.</p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
Data for "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics"
<p>Experimental and precomputed data for the paper "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics" by Oesterle et al. 2020 (DOI: <a href="https://doi.org/10.7554/eLife.54997">10.7554/eLife.54997</a>).</p> <p>The cone bipolar cell data has been described and published in the paper "Inhibition decorrelates visual feature representations in the inner retina" by Franke et al. 2017 (DOI: <a href="https://doi.org/10.1038/nature21394">10.1038/nature21394</a>). </p> <p>This data is both a supplement to the Oesterle et al. paper and the code for this paper.</p> <p>The code is available in this <a href="http://github.com/berenslab/CBC_inference">GitHub repository</a>.</p> <p>We recommend downloading the GitHub repository and to follow the instructions there.</p>
Fluid and kinetic studies of tokamak disruptions using Bayesian optimization
<p>The codes and the data in this directory corresponds to the code and results used in the paper [I. Ekmark et al (2024) J. Plasma Phys., Fluid and kinetic studies of tokamak disruptions using Bayesian optimization, http://arxiv.org/abs/2402.05843]. References to figures below refer to this publication. </p> <p>The optimizations have been performed using the Python package by Fernando Nogueira [https://github.com/bayesian-optimization/BayesianOptimization] and the simulations are performed using the disruptions simulation simulation tool DREAM [https://github.com/chalmersplasmatheory/DREAM, git hash: 0d786e859f6228185ef68b6b3639747e8d96172d], for more information on the latter code visit https://ft.nephy.chalmers.se/dream/.</p> <p>The codes:<br> - BayesianOptimization.py: Runs the optimization, first in fluid and then in isotropic mode. For activated simulations, use the flag "-A".<br> - BlackBox.py: Contains the functions that are run in the optimizations and sets up the simulations.<br> - utils.py: Contains the settings for the simulations as well as some other functions needed in BlackBox.py<br> - ITER.py: Contains all the ITER specific settings.<br> - Exceptions.py: Contains exceptions needed during the simulations. <br> - CostFunction.py: Contains the functions used for evaluating the cost function value for specified values of the representative runaway current, final Ohmic current, current quench time and transported heat fraction.<br> - RunCases.py: Sets up simulations for the cases of table 1 in the paper, as well as for all the optima found.</p> <p>The data:<br> - Optimization results:<br> <br> - Data/OptimizationResults/optresult_fluid.json: Contains the optimization data for the non-activated case using the fluid model. Used to produce figure 1.a. <br> - Data/OptimizationResults/optresult_isotropic.json: Contains the optimization data for the non-activated case using the isotropic model. Used to produce figure 1.b. <br> - Data/OptimizationResults/optresult_fluid_activated.json: Contains the optimization data for the activated case using the fluid model. Used to produce figure 5.a.<br> - Data/OptimizationResults/optresult_isotropic_activated.json: Contains the optimization data for the activated case using the isotropic model. Used to produce figure 5.b.<br> <br> - Data/OptimizationResults/components_fluid.json: Contains the cost function components for each sample from the optimization of the non-activated case using the fluid model. Used to produce figure 2.a. <br> - Data/OptimizationResults/components_isotropic.json: Contains the cost function components for each sample from the optimization of the non-activated case using the isotropic model. Used to produce figure 2.b. <br> - Data/OptimizationResults/components_fluid_activated.json: Contains the cost function components for each sample from the optimization of the activated case using the fluid model. Used to produce figure 6.a.<br> - Data/OptimizationResults/components_isotropic_activated.json: Contains the cost function components for each sample from the optimization of the activated case using the isotropic model. Used to produce figure 6.b.<br> <br> - Cases:<br> - Data/Cases/nonActivatedOpts/fluidOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the non-activated scenario using the fluid model.<br> - Data/Cases/nonActivatedOpts/isoOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the non-activated scenario using the isotropic model.<br> - Data/Cases/activatedOpts/fluidOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the activated scenario using the fluid model.<br> - Data/Cases/activatedOpts/isoOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the activated scenario using the isotropic model.<br> - Data/Cases/[circle, cross, square, triangle]: Contains outputfiles for fluid and isotropic simulations corresponding to the cases presented in table 1.</p>
Experimental result data for primal-dual contextual Bayesian optimization
<p>This dataset is the result of the paper "Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints" published in IEEE Conference on Decision and Control 2023. </p>
The optimal time to approach an unfamiliar object: A Bayesian model
<p>Many organisms take time before approaching unfamiliar objects. This caution forms the basis of some well-known assays in the fields of behavioral ecology, comparative psychology and animal welfare, including quantifying the personality traits of individuals and evaluating the extent of their neophobia. In this paper we present a mathematical model which identifies the optimal time an observer should wait before approaching an unfamiliar object. The model is Bayesian, and simply assumes that the longer the observer goes without being attacked by an unfamiliar object, the lower will be the observer's estimated probability that the object is dangerous. Given the information gained, a time is reached at which the expected benefits from approaching the object begin to exceed the costs. The model not only explains why latency to approach may be repeatable among individuals and vary with the object's appearance, but also why individuals habituate to the stimulus, approaching it more rapidly over repeated trials. We demonstrate the applicability of our model by fitting it to published data on the time taken by chicks to attack artificial caterpillars which share no, one, or two signaling traits with snakes (eyespots and posture). We use this example to show that while the optimal time to attack an unfamiliar object reflects the observer's expectation that the object is dangerous, the rate at which habituation arises is also a function of the observer's certainty in their belief. In so doing, we explain why observers become more rapidly habituated to "weaker" stimuli than "stronger" ones. </p>
The optimal time to approach an unfamiliar object: A Bayesian model
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DMSO-TMP-ACN-H2O Co-solvent Bayesian Optimization with Reproducibility and Gas Analysis via OEMS Data
<p>The zipped files contain the data collected and used for the Bayesian optimization (BO) of Coulombic efficiency (and discharge capacity) from the exploration of 4 co-solvents (dimethyl sulfoxide, trimethyl phosphate, acetonitrile, and water) and 2 salts (lithium perchlorate and LiTFSI).</p> <p>The cycling data and the BO clients are contained in BayesianOptimization.zip.</p> <p>The gas analysis data via online electrochemical mass spectrometry (OEMS) are contained in OEMS_data.zip.</p> <p>The cycling data of select repeats from the BO are contained in Reproducibility_data.zip.</p> <p>These are the raw datafiles. Preprocessing and analysis is not included.</p>
Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction"
<p><strong>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</strong></p> <p>This publication contains the research data and example scripts for the paper “Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction” [1]. The research data is found in the directory <code>research_data</code>, the example scripts are found in the directory <code>example_scripts</code>.</p> <p>The research data contains all necessary information to be able to reconstruct the figures and values given in the paper, as well as all result figures shown. Where possible, the directories contain the necessary scripts to recreate the results themselves, up to stochastic variations.</p> <p>The example scripts are intended to show how one can (i), perform a least-square type optimization of a model function (here we focus our efforts on the analytical model functions MGH17 and Gauss3, as described in the paper) using various methods (BTVO, LM, BO, L-BFGS-B, NM, including using derivative information when applicable), and (ii), perform Markov chain Monte Carlo (MCMC) sampling around the found maximum likelihood estimate (MLE) to estimate the uncertainties of the MLE parameter (both using a surrogate model of the actual model function, as well as using the actual model function directly).</p> <p> </p> <p><strong>Research data</strong></p> <p>Contained are directories for the experimental problem GIXRF, and the two analytical model functions MGH17 and Gauss3. What follows is a listing of directories and the contents:</p> <ul> <li><code>gauss3_optimization</code>: Optimization logs for the Gauss3 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_optimization</code>: Optimization logs for the MGH17 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_mcmc_analytical</code>: Scripts for the creation of the plots (does not use an optimization log), as well as plots shown in the paper. This uses the model function directly to perform the MCMC sampling.</li> <li><code>mgh17_mcmc_surrogate</code>: Optimization log of the MGH17 function used for the creation of the MCMC plots, scripts for the creation of the plots (use the optimization log), as well as plots shown in the paper. This uses a surrogate model to perform the MCMC sampling.</li> <li><code>gixrf_optimization</code>: <code>benchmark.py</code> file to perform the optimization, the optimization logs for the various methods (BTVO, LM, BO, L-BFGS-B, NM), .npy files and scripts used for the creation of the plots, and the plots shown in the paper.</li> <li><code>gixrf_mcmc_supplement</code>: optimization log used for the creation of the plot, pickle file used for the creation of the plot, script to create the MCMC plot.</li> <li><code>gixrf_optimum_difference_supplement</code>: optimization logs of BTVO optimization of the GIXRF problem, scripts to create the difference/error plots shown for the GIXRF problem in the supplement, and the plots themselves.</li> </ul> <p><strong>Employed software for creating the research data</strong></p> <p>The software used in the creation is:</p> <ul> <li>JCMsuite Analysis and Optimization toolkit, development version, commit d55e99b (the closest commercial release is found in JCMsuite version 5.0.2)</li> <li>A list of Python packages installed (excerpt from <code>conda list</code>, name and version) <ul> <li>corner 2.1.0</li> <li>emcee 3.0.2</li> <li>jax 0.2.22</li> <li>jaxlib 0.1.72</li> <li>matplotlib 3.2.1</li> <li>numba 0.40.1</li> <li>numpy 1.18.1</li> <li>pandas 0.24.1</li> <li>python 3.7.11</li> <li>scikit-optimize 0.7.4</li> <li>scipy 1.7.1</li> <li>tikzplotlib 0.9.9</li> </ul> </li> <li>JCMsuite 4.6.3 for the evaluation of the experimental model</li> </ul> <p> </p> <p><strong>Example scripts</strong></p> <p>This directory contains a few sample files that show how parameter reconstructions can be performed using the JCMsuite analysis and optimization toolbox, with a particular focus on the Bayesian target-vector optimization method shown in the paper.</p> <p>It also contains example files that show how an uncertainty quantification can be performed using MCMC, both directly using a model function, as well as using a surrogate model of the model function.</p> <p>What follows is a listing of the contents of the directory:</p> <ul> <li><code>mcmc_mgh17_analytical.py</code>: performs a MCMC analysis of the MGH17 model function directly, without constructing a surrogate model. Uses <code>emcee</code>.</li> <li><code>mcmc_mgh17_surrogate.py</code>: performs a MCMC analysis of the MGH17 model function by constructing a surrogate model of the model function. Uses the JCMsuite analysis and optimization toolbox.</li> <li><code>opt_gauss3.py</code>: performs a parameter reconstruction of the Gauss3 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>opt_mgh17.py</code>: performs a parameter reconstruction of the MGH17 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>util/model_functions.py</code>: contains the MGH17 and Gauss3 model functions, their (automatic) derivatives, and objective functions used in the optimizations.</li> </ul> <p><strong>Requirements to execute the example scripts</strong></p> <p>These scripts have been developed and tested under Linux, Debian 10. We have tried to make sure that they would also work in a Windows environment, but can unfortunately give no guarantees for that.</p> <p>We mainly use Python to run the reconstructions. To execute the files, a few Python packages have to be installed. In addition to the usual scientific Python stack (NumPy, SciPy, matplotlib, pandas, etc.), the packages <code>jax</code> and <code>jaxlib</code> (for automatic differentiation of Python/NumPy functions), <code>emcee</code> and <code>corner</code> (for MCMC sampling and subsequent plotting of the results) have to be installed.</p> <p>This can be achieved for example using pip, e.g.</p> <pre><code>pip install -r requirements.txt</code></pre> <p>Additionally, JCMsuite has to be installed. For this you can visit [2] and download a free trial version.</p> <p>On Linux, the installation has to be added to the PATH, e.g. by adding the following to your <code>.bashrc</code> file:</p> <pre><code>export JCMROOT=/FULL/PATH/TO/BASE/DIRECTORY export PATH=$JCMROOT/bin:$PATH export PYTHONPATH=$JCMROOT/ThirdPartySupport/Python:$PYTHONPATH</code></pre> <p> </p> <p><strong>Bibliography</strong></p> <p>[1] <span>M. Plock</span>, <span> K. Andrle</span>, <span> S. Burger</span>, <span> P.-I. Schneider</span>, <span>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</span>. <em>Adv. Theory Simul.</em> <strong><span>5</span></strong>, 2200112 (2022).</p> <p>[2] https://jcmwave.com/</p>
Exploring the conformers of an organic adsorbate on a metal cluster with Bayesian optimization
<p>[1] Lincan Fang, Xiaomi Guo, Milica Todorovic, Patrick Rinke, Xi Chen. Exploring the conformers of an organic adsorbate on a metal cluster with Bayesian optimization</p> <p>This dataset shows 1000 sampling points in the configuration space of the cysteine ligand binding with the S site of Au25S18 cluster. For Au25S18 cluster, there are two inequivalent S sites (Au-S-Au-S-Au-S-Au arrow top and side). Here, the binding S site is on "arrow side", we named it "system B". The sampling method is the Bayesian Optimization Structure Search (BOSS), each sampling point consists of structure features and DFT structure energy. The structure features are five dihedral angles of cysteine ligand, d1 (Au-S-C1-C2), d2 (S-C1-C2-N), d3 (C1-C2-N-H), d4 (C1-C2-C3-O1), and d5 (C2-C3-O1-H). The energy was calculated by FHI-aims with PBE functional, tier 2 setting with many-body dispersion corrections. Due to the confined configuration space of this system, we use the energy transformation method (see in manuscript) to tackle one sampling structure that cannot be simulated by DFT or has a huge high DFT energy. For additional details on BOSS search please refer to [1].</p> <p>The stable local minimum structures from BOSS can be found in NOMAD: https://dx.doi.org/10.17172/NOMAD/2022.08.20-1</p> <p> </p>
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction
<p><span>This dataset includes the jet grout data compiled from published case histories for Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction.</span></p>
MATLAB codes for paper: UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings
<p>These codes belong to the following paper. Please cite our work.</p> <p>Berghout T, Benbouzid M. UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings. <em>Electronics</em>. 2024; 13(12):2419. https://doi.org/10.3390/electronics13122419</p>
FIGURE 2 in A reassessment of apheloriine millipede phylogeny: additional taxa, Bayesian inference, and direct optimization (Polydesmida: Xystodesmidae)
FIGURE 2. Preferred phylogram reconstructed using Bayesian inference from the new data set comprising an additional 29 species. Names in bold are new taxa sequenced for this study—15 of which are new species, indicated by "n. sp." Harmonic mean likelihood ≥ -18315.52. Thickened, black branches denote posterior clade probability values ≥ 0.95. Boxes above branches indicate clades supported by different analyses: "Circle B", Bayesian inference, posterior clade probability ≥ 0.95; "Circle P", Direct optimization, node recovered in ≥ 95% of trees generated under the preferred '121' gap-opening and extension parameters summarized in a 95% majority rule consensus; and "Circle M", Maximum likelihood, nonparametric bootstrap clade support values ≥ 95%. Numbers in boxes indicate support values <95%. Light gray box = Southern clade; medium gray box = Appalachioria species; dark gray box = Brachoria species. Exemplar species names followed by specimen numbers in parentheses. Gyalostethus monticolens branch abbreviated with a break for ease of illustration (branch length = 1.047, about 1.6X greater than shown).
Bayesian Analysis of Quasar Lightcurves with a Running Optimal Average: PyROA Fits to COSMOGRAIL Data
<p>Available as .zip files are the data/results of using PyROA to fit to the COSMOGRAIL gravitationally lensed quasar data. In each .zip are the individual lightcurves for each image as a .dat file, a plot of the results, a corner plot of the sampled parameters and three pickle objects. These are samples.obj, samples_flat.obj and X_t.obj.</p> <ul> <li>The first of these, samples.obj, contains all the MCMC samples in an array with shape (Nsamples, Nwalkers, Ndim).</li> <li>The second, samples_flat.obj, contains the flattened samples, where the burn-in has been removed and is an array with shape (Nsamples_final, Ndim), where Nsamples_final is the number of samples with burn-in removed. This was used to generate the corner plot and obtain the best fit parameters.</li> <li>The last, X_t.obj, contains the driving lightcurve, X(t), as described in Donnan et al. 2021. This is an array of the form [t, X, X_errs], where X is the value of the driving lightcurve at time, t, with errors, X_errs.</li> </ul> <p>For objects with more than two images, folders where other images were the reference are included. The sigma parameter in the corner plot is the extra error added to the flux data which is labelled as <em>s<sub>i</sub></em> in the paper. The original data file downloaded from the COSMOGRAIL website is also included as a csv file.</p>
Figures 5–6. Optimal trees obtained under Bayesian analyses. Fig. 5. Mkv model. Fig. 6 in Phylogenetic analysis of Micrathena and Chaetacis spiders (Araneae: Araneidae) reveals multiple origins of extreme sexual size dimorphism and long abdominal spines
Figures 5–6. Optimal trees obtained under Bayesian analyses. Fig. 5. Mkv model. Fig. 6. MkvG model. Posterior probabilities values are indicated below branches.
Optimization scripts used for "Bayesian optimization of laser-plasma accelerators assisted by reduced physical models"
<p>This dataset contains the optimization scripts needed to reproduce the results from the article "Bayesian optimization of laser-plasma accelerators assisted by reduced physical models" by A. Ferran Pousa, S. Jalas, M. Kirchen, A. Martinez de la Ossa, M. Thévenet, J. Larson, S. Hudson, A. Huebl, J.-L. Vay, and R. Lehe.</p>
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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.