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  • README.txt

    414 bytes, text/plain

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    Date added: 13/03/25

  • Matt_s_comments.pdf

    316 KB, PDF document

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    Date added: 13/03/25

  • mopp016.pdf

    956 KB, PDF document

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  • s41598-022-16235-x.pdf

    2.97 MB, PDF document

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  • Saed_Alrawash_SNA24.pdf

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  • Saed_Alrawash_SNA24_ma_comments_1_.pdf

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  • Saed_IEEE_V1_4_.pdf

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  • SNA_MCA_Saed_Alrawash_ppt_081.pptx

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  • 04photon_neutron.xlsx

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  • MC_simu.xlsx

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  • predictionFull2.xlsx

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  • Training_MissingData.xlsx

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    Date added: 13/03/25

  • CoriumThickness_Gaussian_Process_Regression_Rational_Quadratic_GPR.png

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  • optimiser_bayesian.png

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  • Optimizable_GPR.png

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  • Optimizable_response_plot.png

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  • predictions_Optimizable.jpg

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  • response_plot_modified.jpg

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  • response_plot_v1.jpg

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  • summary_of_the_trained_model.png

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  • summary_optimizable.png

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  • True_Vs_Predicted_thickness.jpg

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  • MC_simu.xlsx

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    Date added: 13/03/25

  • NeutronSource_Modified.zip

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    Date added: 13/03/25

  • sm_neutron.csv

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    Date added: 13/03/25

  • sna_modified_word.docx

    253 KB, Word document

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  • u_neutron.csv

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    Date added: 13/03/25

  • 1-s2.0-S0168900219310757-main.pdf

    1.85 MB, PDF document

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    Date added: 13/03/25

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    1.38 MB, PDF document

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    Date added: 13/03/25

  • 10.1515_phys-2022-0006.pdf

    3.34 MB, PDF document

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    Date added: 13/03/25

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    3.25 MB, PDF document

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    Date added: 13/03/25

  • 1_Development_of_Detailed_Investigation_inside_PCV_IRID_2019.pdf

    1.95 MB, PDF document

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    Date added: 13/03/25

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    Date added: 13/03/25

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    1.84 MB, PDF document

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    Date added: 13/03/25

  • A_Deep_Learning_Approach_to_Anomaly_Detection_in_Nuclear_Reactors.pdf

    3.48 MB, PDF document

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    Available under license: CC BY

    Date added: 13/03/25

  • c2ja10230g.pdf

    553 KB, PDF document

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    Date added: 13/03/25

  • FDR2019_1100.pdf

    533 KB, PDF document

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    Date added: 13/03/25

  • Final_article_robot_radiation_tolerance_testing.pdf

    972 KB, PDF document

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    Date added: 13/03/25

  • High-Performance_Single-Crystal_Diamond_Detector_f.pdf

    3.11 MB, PDF document

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    Date added: 13/03/25

  • idea_for_.txt

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    Date added: 13/03/25

  • ideas_for_article.docx

    526 KB, Word document

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    Date added: 13/03/25

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    394 KB, PDF document

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    Date added: 13/03/25

  • Neural_Network_Approaches_for_Mobile_Spectroscopic.pdf

    394 KB, PDF document

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    Date added: 13/03/25

  • proposal_outlines.txt

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    Date added: 13/03/25

  • s41597-020-00672-2.pdf

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    Date added: 13/03/25

  • predictions_Optimizable.fig

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    Date added: 13/03/25

  • RegressionLearnerSession1.mat

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    Date added: 13/03/25

  • response_plot.fig

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    Date added: 13/03/25

  • trainRegressionModel.asv

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  • true_predict_gpr.fig

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DOI

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Simulation for Prediction of Fuel Debris Location in Fukushima Nuclear Power Plant using Machine Learning

Dataset

Description

This work has been published in Paris SNA + MC 2024 Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo.

The folder contains:
1- Monte Carlo simulation results
2- Excel files contain the extracted data from the results files
3- figures
4- reference articles

The work used machine learning applications, so the folder contains training data and prediction data with their .m files
Date made available2024
PublisherLancaster University

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