[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122137-en":3,"doc-seo-122137-105":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":4,"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},122137,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Knowledge-guided Machine Learning - Current Trends and Future Prospects","This paper provides an overview of scientific modeling and evaluates the complementary strengths and weaknesses of machine learning (ML) methods relative to process-based models. It introduces scientific knowledge-guided machine learning (KGML), which combines scientific knowledge with data-driven ML to improve generalizability, scientific consistency, and result explainability. The discussion covers KGML research facets, including knowledge types, knowledge-ML integration forms, and incorporation methods, followed by use-case categories in environmental sciences with illustrative examples.","arXiv :2403 . 15989v2 [ cs .LG] 1 May 2024  \nKnowledge-guided Machine Learning: Current Trends and Future Prospects  \nAnuj Karpatne 1 , Xiaowei Jia2 and Vipin Kumar3  \n1 Department of Computer Science, Virginia Tech, [E-mail: karpatne@vt.edu](E-mail: karpatne@vt.edu).  \n2 Department of Computer Science, University of Pittsburgh, E-mail: [xiaowei@pitt.edu](xiaowei@pitt.edu).  \n3 Department of Computer Science and Engineering, University of Minnesota, [E-mail: kumar001@umn.edu](E-mail: kumar001@umn.edu).  \nAbstract  \nThis paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based models. It also provides an introduction to the current state of research in the emerging field of scientific knowledge-guided machine learning (KGML) that aims to use both scientific knowledge and data in ML frameworks to achieve better generalizability, scientific consistency, and explainability of results. We discuss different facets of KGML research in terms of the typeof scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML. We also discuss some of the common categories of use cases in environmental sciences where KGML methods are being developed, using illustrative examples in each category.  \n1. Introduction  \nAs advances in artificial intelligence (AI) and machine learning (ML) continue to revolutionize mainstream applications in commercial problems, there is a growing excitement in the scientific community to harness the power of ML for accelerating scientific discovery [1, 86, 151, 181] . This is especially true in environmental sciences that are rapidly transitioning from being data-poor to data-rich, e.g., with the ever-increasing volumes of environmental data being collected by Earth observing satellites, in-situ sensors, and those generated by model simulations (e.g., climate model runs [113]) . Similar to how recent developments in ML has transformed how we interact with the information on the Internet, it is befitting to ask how ML advances can enable Earth system scientists to transform a fundamental goal in science, which is to build better models of physical, biological, and environmental systems.  \nThe conventional approach for modeling relationships between input drivers and response variables is to use process-based models rooted in scientific equations. Despite their ability to leverage the mechanistic understanding of scientific phenomena, process-based models suffer from several shortcomings limiting their adoption in complex real-world settings, e.g., due to imperfections in model formulations (or modeling bias), incorrect choices of parameter values in equations, and high computational costs in running high-fidelity simulations. In response to these challenges, ML methods offer a promising alternative to capture statistical relationships between inputs and outputs directly from data. However,“black-box” ML models, that solely rely on the supervision contained in data, show limited generalizability in scientific problems, especially when applied to out-of-distribution data. One of the reasons for this lack of generalizability is the limited scale of data in scientific disciplines in contrast to mainstream applications of AI and ML where large-scale datasets in computer vision and natural language modeling have been instrumental in the success of state-of-the-art AI/ML models. Another fundamental deficiency in black-box ML models is their tendency to produce results that are inconsistent with existing scientific theories and their inability to provide a mechanistic understanding of discovered patterns and relationships from data, limiting their usefulness in science.  \n2  \n|  | Process-based Model |  |\n| --- | --- | --- |\n| \u003Cbr>Scientific Equations\u003Cbr>\u003Cbr>\u003Cbr>System States\u003Cbr>\u003Cbr>\u003Cbr>Model parameters |  |  |\n\nTime Series Time Series  \n","cbCaia9GvC5tIXvS","https://ap.wps.com/l/cbCaia9GvC5tIXvS","pdf",1193075,1,29,"English","en",105,"# Introduction\n## Scientific modeling: process-based vs black-box ML\n## Scientific knowledge-guided machine learning (KGML)\n## Paper goals and structure","[{\"question\":\"What problem does KGML aim to solve in scientific machine learning?\",\"answer\":\"KGML targets the limited generalizability and scientific inconsistency of black-box ML by incorporating scientific knowledge alongside data in ML frameworks.\"},{\"question\":\"How does the paper differentiate process-based models from ML models?\",\"answer\":\"It contrasts process-based models grounded in scientific equations with ML methods that learn statistical input-output relationships from data, noting limitations of black-box ML such as weak out-of-distribution performance and lack of mechanistic interpretability.\"},{\"question\":\"What main dimensions does the paper use to organize KGML research?\",\"answer\":\"KGML research is discussed through three perspectives: the type of scientific knowledge used, the form of knowledge-ML integration, and the method for incorporating scientific knowledge into ML.\"}]","Knowledge-guided Machine Learning - Current Trends and Future Prospects | PDF",1785808994,73,{"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},"knowledge-guided-machine-learning-current-trends-and-future-prospects","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/knowledge-guided-machine-learning-current-trends-and-future-prospects/122137/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does KGML aim to solve in scientific machine learning?","Question",{"text":75,"@type":76},"KGML targets the limited generalizability and scientific inconsistency of black-box ML by incorporating scientific knowledge alongside data in ML frameworks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper differentiate process-based models from ML models?",{"text":80,"@type":76},"It contrasts process-based models grounded in scientific equations with ML methods that learn statistical input-output relationships from data, noting limitations of black-box ML such as weak out-of-distribution performance and lack of mechanistic interpretability.",{"name":82,"@type":73,"acceptedAnswer":83},"What main dimensions does the paper use to organize KGML research?",{"text":84,"@type":76},"KGML research is discussed through three perspectives: the type of scientific knowledge used, the form of knowledge-ML integration, and the method for incorporating scientific knowledge into ML.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]