[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122954-en":3,"doc-seo-122954-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122954,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Uncertainty Quantification in Machine Learning Models Via Gaussian Process Regression - A Comparative Study - Poster","Poster presents an uncertainty quantification framework for machine learning models addressing reliability and trust in synchrotron X-ray diffraction (SXRD) applications. The study extends uncertainty quantification to predict martensitic phase volume fraction in Ti-6Al-4V using Gaussian process regression (GPR) on 2D diffraction images. A comparative set of kernels is evaluated with PCA-based dimensionality reduction, reconstructing 2D images from principal components. Results report original versus predicted phase fraction with 95% prediction intervals and performance measured by RMSE, including HPC runtime comparisons. Conclusions emphasize additive exponential kernels’ reduced computation time while preserving uncertainty quantification, with future work targeting larger sample sizes and higher dimensions.","Case Western Reserve University  \nScholarly Commons @ Case Western Reserve University  \n\n| Faculty Scholarship |\n| --- |\n| Summer 8-6-2024\u003Cbr>Uncertainty Quantification in Machine Learning Models Via Gaussian Process Regression: A Comparative Study\u003Cbr>Ayorinde E. Olatunde\u003Cbr>Case Western Reserve University, [aeo49@case.edu](aeo49@case.edu)\u003Cbr>Weiqi Yue\u003Cbr>Case Western Reserve University, [wxy215@case.edu](wxy215@case.edu)\u003Cbr>[Pawan K. Tripathi](Pawan K. Tripathi)\u003Cbr>Case Western Reserve University, [pkt19@case.edu](pkt19@case.edu)\u003Cbr>[Roger H. French](Roger H. French)\u003Cbr>Case Western Reserve University, [rxf131@case.edu](rxf131@case.edu)\u003Cbr>Anirban Mondal\u003Cbr>Case Western Reserve University, [axm912@case.edu](axm912@case.edu)[ ](axm912@case.edu)[Author](Author)([s](s)) ORCID Identifier:\u003Cbr>Follotrhinisdaendaitinouneall loartkusnadte: [https://commons.case.edu/facultyworks](https://commons.case.edu/facultyworks)\u003Cbr> Part of the Data Science Commons, Materials Science and Engineering Commons, and the Statistics and Probability Commons\u003Cbr> |\n\nRecommended Citation  \nOlatunde, Ayorinde E.; Yue, Weiqi; Tripathi, Pawan K.; French, Roger H.; and Mondal, Anirban, \"Uncertainty Quantification in Machine Learning Models Via Gaussian Process Regression: A Comparative Study\"(2024) . Faculty Scholarship. 345.  \n[https://commons.case.edu/facultyworks/345](https://commons.case.edu/facultyworks/345)  \nThis Poster is brought to you for free and open access by Scholarly Commons @ Case Western Reserve University. It has been accepted for inclusion in Faculty Scholarship by an authorized administrator of Scholarly Commons @ Case Western Reserve University. For more information, please [contact digitalcommons@case.edu](contact digitalcommons@case.edu).  \nCWRU authors have made this work freely available. Please tell us how this access has benefited or impacted you!  \nMaterials Data Science for Stockpile Stewardship  \nCOE: US-Department of Energy-NNSA Award  \nUncertainty Quantification in Machine Learning Models Via Gaussian Process Regression: A Comparative  \nStudy  \nAyorinde E. Olatunde 1,4 , Weiqi Yue 3,4 , Pawan K. Tripathi 2,4 , Roger H. French2,3,5 , Anirban Mondal 1,4,*  \n1 Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University , Cleveland, OH, 44106, USA  \n2 Department of Materials Science and Engineering, Case Western Reserve University , Cleveland, OH, 44106, USA  \n3 Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA  \n4 Materials Data Science for Stockpile Stewardship: Center of Excellence, Case Western Reserve University, Cleveland, OH, 44106, USA  \n* [axm912@case.edu](axm912@case.edu)  \n1. INTRODUCTION  \nBackground of the Study:  \n● Synchrotron X-ray Diffraction (SXRD) problems are solved using Machine Learning (ML) . Uncertainty Quantification (UQ) ensures model reliability and trust.  \nMotivation of the Study:  \n● QU is harder feature space increases [1] . Additive covariance kernels help with UQ in large feature spaces [2]  \nGoal of the Study:  \n● Extend UQ for predicting 􁶔-phase volume fraction in Ti-6Al-4V alloy [3] alloy using 2D diffraction images via Gaussian Process Regression (GPR) to higher feature spaces.  \n2. EXPERIMENTAL SET UP FOR DATA COLLECTION  \nThe experiment involved generating time-series SXRD diffraction patterns of the sample by directing a beam of X-rays onto it as it underwent heating and cooling.  \n3. METHODOLOGY  \nExperimentation  \nOut of four experiments conducted, we used data set from three of them and a combination of the three data sets  \nApply PCA as dimension reduction technique for computational efficiency   \nData   Ingestion &  \nReconstruction  \n2048 X 2048  \nAn example of reconstructed 2D diffraction image using 5 PC  \nModelling with  \ncomparative Kernels &    \nQuantifying Uncertainties  \nUsed GPR, leveraging its mean and covariance functions for probability distribution over functional relationships, for predicti","cbCaicfQOG5Y4Thv","https://ap.wps.com/l/cbCaicfQOG5Y4Thv","pdf",1006032,1,2,"English","en",105,"# Introduction\n## Background and motivation\n## Goal\n# Experimental set up for data collection\n# Methodology\n## Data ingestion and reconstruction\n## Modelling with comparative kernels\n# Results\n## Prediction intervals and RMSE\n## Runtime and kernel performance\n# Conclusions & future direction\n# References\n# Acknowledgement","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"The study aims to extend uncertainty quantification for predicting martensitic phase volume fraction in Ti-6Al-4V using 2D diffraction images with Gaussian process regression in higher feature spaces.\"},{\"question\":\"How is the data prepared for modelling?\",\"answer\":\"Time-series SXRD diffraction patterns are generated, then 2D images are reconstructed and PCA is applied for dimensionality reduction to improve computational efficiency.\"},{\"question\":\"Which method is used for uncertainty quantification and comparisons?\",\"answer\":\"Gaussian process regression is used, leveraging its mean and covariance to produce predictions and uncertainty quantification, while multiple kernel structures are compared under different datasets.\"}]","Uncertainty Quantification in Machine Learning Models Via Gaussian Process Regression - A Comparative Study - Poster | PDF",1785813857,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"uncertainty-quantification-in-machine-learning-models-via-gaussian-process-regression-a-comparative-study-poster","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/uncertainty-quantification-in-machine-learning-models-via-gaussian-process-regression-a-comparative-study-poster/122954/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"The study aims to extend uncertainty quantification for predicting martensitic phase volume fraction in Ti-6Al-4V using 2D diffraction images with Gaussian process regression in higher feature spaces.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the data prepared for modelling?",{"text":80,"@type":76},"Time-series SXRD diffraction patterns are generated, then 2D images are reconstructed and PCA is applied for dimensionality reduction to improve computational efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"Which method is used for uncertainty quantification and comparisons?",{"text":84,"@type":76},"Gaussian process regression is used, leveraging its mean and covariance to produce predictions and uncertainty quantification, while multiple kernel structures are compared under different datasets.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":29,"slug":137},19,"General","general"]