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5 results for “bayesian uncertainty quantification”
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package
<p>Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.</p>
YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
<p>This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.</p>
Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations
<p>The data contained in the uploaded '.zip' file is for some of the plots in the paper ‘Duan Y*, Eaton MD, Bluck MJ, 2021, Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations, International Journal of Heat and Fluid Flow.’ (accepted)</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>
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.