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51 results for “uncertainty quantification”
Towards Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance
<p>This repository contains the implementation of <strong>Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance</strong>.</p> <p>The provided Python script demonstrates the construction of the MCMC (Markov Chain Monte Carlo) method and illustrates how to use MCMC for generating hemolysis distributions. Please note that the actual hemolysis calculations should be performed using your preferred CFD (Computational Fluid Dynamics) software.</p> <p>If there are any questions, please contact:</p> <p>blum@ame.rwth-aachen.de </p>
Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification
<p>This project combines the emerging field of Domain Adaptation with Uncertainty Quantification, working towards applying machine learning to real scientific datasets with limited labelled data. For this project, simulated images of strong gravitational lenses are used as source and target dataset, and the Einstein radius θ E and its uncertainty are determined through regression.</p> <p>Applying machine learning in science domains such as astronomy is difficult. With models trained on simulated data being applied to real data, models frequently underperform - simulations cannot perfectlty capture the true complexity of real data. Enter domain adaptation (DA). The DA techniques used in this work use Maximum Mean Discrepancy (MMD) Loss to train a network to being embeddings of labelled "source" data gravitational lenses in line with unlabeled "target" gravitational lenses. With source and target datasets made similar, training on source datasets can be used with greater fidelity on target datasets.</p> <p>Scientific analysis requires an estimate of uncertainty on measurements. We adopt an approach known as mean-variance estimation, which seeks to estimate the variance and control regression by minimizing the beta negative log-likelihood loss. To our knowledge, this is the first time that domain adaptation and uncertainty quantification are being combined, especially for regression on an astrophysical dataset.</p>
Data for "Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1"
<p>The domain file and surface data file that used to run ELMv1, and processed ISIMP2a runoff data that used in <a href="https://gmd.copernicus.org/preprints/gmd-2021-401/">https://gmd.copernicus.org/preprints/gmd-2021-401/</a></p> <p>ELM_runoff_parameter_post.nc contains the ELM runoff generation relevant parameter posteriors at a global half degree spatial resolution.</p>
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>
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
Open the record for dataset details and reuse information.
Data from: Quantification and mitigation of uncertainties in thermal conductivity measurements using a modified ASTM D5470 thermal resistance tester
Open the record for dataset details and reuse information.
MME-only models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME-only ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME-only models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME-only ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME/CRPS models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:</p><p>--uniformize=1</p><p>--predictor_norm_type_string="z_score"</p><p>--vector_target_norm_type_string=""</p><p>--scalar_target_norm_type_string=""</p><p> </p><p>Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
WAM-IPE uncertainty quantification data
<p>In each file, one or several specific WAM-IPE outputs (quantities of interest, QoI) and the associated parameters used in latent space to generate synthetic drivers. The parameters in latent space and the QoIs can be used to build polynomial chaos expansion based surrogate model.</p>
Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100Ⓡ
<p>Scripts and datasets that were used to obtain figures 4B, 6, 7, 8, 9, 10, and 11.</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>
Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning
<p>Data and code underlying the findings reported in the paper titled "Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning."</p>
Data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling"
<p>This is a data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling."</p>
Predictive Modeling of Bearing Degradation: LSTM Neural Networks for Uncertainty Quantification
<p>These MATLAB codes are part of a research project focused on predicting bearing degradation through vibration measurements. The codes implement LSTM (Long Short-Term Memory) neural network models trained under different objectives, including uncertainty quantification and RMSE (Root Mean Square Error) minimization. The objective of the research is to compare the performance of these models in predicting bearing health and assessing the associated uncertainty.</p> <p><strong>Note:</strong> The current codes are under embargo access as the corresponding paper has been submitted to the ESCA 11 conference. The codes will be made openly accessible upon acceptance of the paper and during the presentation dates. Please cite our paper when using these codes.</p>
Snakemake report for manuscript "Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing"
<p>For viewing the report, unzip the file and open index.html in your browser.</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>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.