[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118735-en":3,"doc-seo-118735-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},118735,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Adapting Data-Driven Techniques to Improve Surrogate Machine Learning Model Performance - Paper","This paper demonstrates the adaptation of three established methods for surrogate machine learning model development: data augmentation, custom loss functions, and fine-tuning of pre-trained models. The study targets a surrogate model intended to emulate a traditional UK nuclear engineering model, where limited training data had previously reduced performance. Results show that adding these techniques yields substantial gains, with the best improvements observed when fine-tuning existing models. Across five pre-trained surrogate models, combining all three approaches delivers at least a 38% performance improvement.","The University of Manchester Research  \nAdapting Data-Driven Techniques to Improve Surrogate Machine Learning Model Performance  \nDOI:  \n10.1109/ACCESS.2023.3253429  \nDocument Version  \nFinal published version  \nLink to publication record in Manchester Research Explorer  \nCitation for published version (APA):  \nRhys jones, H. , Popescu, A. C. , Sulehman, Y. , & Mu, T. (2023) . Adapting Data-Driven Techniques to Improve Surrogate Machine Learning Model Performance. IEEE Access, 11 , 23909-23925.  \n[https://doi.org/10.1109/ACCESS.2023.3253429](https://doi.org/10.1109/ACCESS.2023.3253429)  \nPublished in:  \nIEEE Access  \nCiting this paper  \nPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Research Explorer are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTakedown policy  \nIf you believe that this document breaches copyright please refer to the University of Manchester’s Takedown Procedures [[http://man.ac.uk/04Y6Bo](http://man.ac.uk/04Y6Bo)] or [contact openresearch@manchester.ac.uk](contact openresearch@manchester.ac.uk) providing relevant details, so  \nwe can investigate your claim.  \nDownload date:02 . Aug. 2026  \nReceived 31 January 2023, accepted 23 February 2023, date of publication 6 March 2023, date of current version 14 March 2023. Digital Object Identifier 10.1109/ACCESS.2023.3253429  \nAdapting Data-Driven Techniques to Improve Surrogate Machine Learning  \nModel Performance  \nHUW RHYS JONES, ANDREI C. POPESCU, YUSUF SULEHMAN, AND TINGTING MU  \nDepartment of Computer Science, The University of Manchester, M13 9PL Manchester, U.K.  \nCorresponding author: Huw Rhys Jones ([huw.jones@manchester.ac.uk](huw.jones@manchester.ac.uk))  \nThis work was supported in part by the U.K. Research and Innovation, and in part EDF Energy.  \nABSTRACT We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions and fine-tuning of pre-trained models. Each of these methods have seen widespread use in the field of machine learning, however, here we apply them specifically to surrogate machine learning model development. The machine learning model that forms the basis behind this work was intended to surrogate a traditional engineering model used in the UK nuclear industry. Previous performance of this model has been hampered by poor performance due to limited training data. Here, we demonstrate that through a combination of additional techniques, model performance can be significantly improved. We show that each of the aforementioned techniques have utility in their own right and in combination with one another. However, we see them best applied when used to fine-tune existing models. Five pre-trained surrogate models produced prior to this study were further trained using an augmented dataset and with our custom loss function. Through the combination of all three techniques, we see an improvement of at least 38% in performance across the five models.  \nINDEX TERMS Advanced gas-cooled reactor, convolutional neural network, data analysis, data augmentation, data science, graphite, loss function, machine learning, nuclear, regression, supervised learning, surrogate model, pre-trained models, fine-tuning.  \nI. INTRODUCTION  \nA machine learning surrogate (MLS) is a model which aims to explain natural or mathematical phenomena which can already be explained using an existing model. Using data from the original model, machine learning techniques are used to produce an optimised MLS model. The adv","cbCaiuLBnRwleZOr","https://ap.wps.com/l/cbCaiuLBnRwleZOr","pdf",3180823,2,1,18,"English","en",105,"# Abstract\n# Introduction\n## Machine learning surrogate concept and trade-offs\n## Related work and overfitting considerations\n# Methods and findings","[{\"question\":\"What three data-driven techniques are adapted for surrogate machine learning model development?\",\"answer\":\"The paper adapts data augmentation, custom loss functions, and fine-tuning of pre-trained models to surrogate machine learning development.\"},{\"question\":\"Why was the surrogate model’s performance limited before this study?\",\"answer\":\"Performance was hampered by poor results caused by limited training data, which increases risks such as overfitting.\"},{\"question\":\"How much improvement is reported when combining the three techniques?\",\"answer\":\"Combining data augmentation, custom loss functions, and fine-tuning improves performance by at least 38% across five pre-trained surrogate models.\"}]","Adapting Data-Driven Techniques to Improve Surrogate Machine Learning Model Performance - Paper | PDF",1785719985,45,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"adapting-data-driven-techniques-to-improve-surrogate-machine-learning-model-performance-paper","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/adapting-data-driven-techniques-to-improve-surrogate-machine-learning-model-performance-paper/118735/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What three data-driven techniques are adapted for surrogate machine learning model development?","Question",{"text":76,"@type":77},"The paper adapts data augmentation, custom loss functions, and fine-tuning of pre-trained models to surrogate machine learning development.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why was the surrogate model’s performance limited before this study?",{"text":81,"@type":77},"Performance was hampered by poor results caused by limited training data, which increases risks such as overfitting.",{"name":83,"@type":74,"acceptedAnswer":84},"How much improvement is reported when combining the three techniques?",{"text":85,"@type":77},"Combining data augmentation, custom loss functions, and fine-tuning improves performance by at least 38% across five pre-trained surrogate models.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]