[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117905-en":3,"doc-seo-117905-105":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},117905,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The Potential of Scientific Machine Learning in Geotechnics and Engineering Geology - Introduction and Applications","Scientific machine learning combines data-driven machine learning with physics-based models to exploit the strengths of both approaches in geotechnics and engineering geology. The text explains how data-driven methods can be hindered by limited data and weak interpretability, while physics-based simulations can struggle with simplifying assumptions, parameter estimation, and computational cost. By integrating known physics with machine learning, the approach can improve accuracy, use data more efficiently, and enhance interpretability. It also outlines why unknown physics and uncertainty are common in soil and rock processes, and highlights potential use in hazards such as landslide and rockfall prediction.","\\#SINTEFblog 􀀀  \n\\#BUILDING  \n\\#COMMUNITY  \n\\#DIGITAL  \nThe Potential of Scientific Machine Learning in Geotechnics and Engineering  \nGeology  \nBY YARED BEKELE  \nAPRIL 19, 2023  \nCOMMENTS 􀀀 0  \nMachine learning has rapidly become a popular research topic in science and engineering due to its vast potential for solving complex problems and improving processes. The number of research projects and publications related to this field is growing rapidly as more researchers recognize its potential benefits. Machine learning has also found applications in geotechnics and engineering geology, with researchers using it in various ways to improve processes and solve complex problems, such as predicting the behaviour of soilsand rocks.  \nCombining data-driven and physics-based models could help in predicting landslides better than what is possible based on  \neach of the approaches separately. Photo: Shutterstock  \nAlthough promising results have been achieved in various fields, including geotechnics and engineering geology, further research is necessary to fully realize the potential of machine learning and address current limitations and scepticism. One area that requires more research is scientific machine learning, which remains largely unexplored, particularly in geotechnics and engineering geology.  \nWhat is Scientific Machine Learning?  \nScientific machine learning is an approach that combines traditional data-driven machine learning techniques with physics-based models to leverage the strengths of both approaches. Data-driven models are machine learning models that learn patterns and relationships directly from data, without requiring a priori knowledge of the underlying physics or mechanisms. An example of a data-driven model in geotechnics and engineering geology is the prediction of soil and rock properties by analysing large datasets of site investigation data.  \nOn the other hand, physics-based models are mathematical models that describe the behaviour of a system  \nbased on fundamental principles and laws of physics. Physics-based models are usually formulated in the form of algebraic equations and partial differential equations, which are solved using analytical or numerical methods. Examples in geotechnics and engineering geology include conservation equations for soils/rocks as multiphase media (mass, momentum and energy) and constitutive models (e.g., Mohr-Coulomb, HoekBrown, etc. ) .  \nScientific machine learning can help overcome the limitations of data-driven and physics-based models. Some of the limitations that make the use of data-driven models challenging include limited data availability and limited interpretability. On the other hand, physics-based models suffer from limitations, including assumptions/simplifications that don’t capture the behaviour of real-world systems, difficulty in model parameter estimation and computational complexity.  \nCombining data-driven and physics-based models in scientific machine learning can overcome their limitations, for example, through a more efficient use of data, by improving accuracy and providing interpretability to predictions. As an emerging topic, scientific machine learning is referred to using various terminologies, such as physics-informed neural networks, physics-informed machine learning and theoryguided machine learning.  \nScientific ML at the intersection of data-driven and physics-based models  \nKnown vs Unknown Physics in the Geosciences  \nUncertainty is an inherent part of scientific inquiry and knowledge. Most scientific disciplines rely on observations, measurements, and experiments to test hypotheses and theories. However, some disciplines may encounter more uncertainty than others due to factors such as the complexity of the materials or systems they study, limitations in data availability and acquisition methods, and the difficulty of testing new hypotheses. Geotechnics and engineering geology are examples of such disciplines, which involve a significant ","cbCaieAHwULn0kKq","https://ap.wps.com/l/cbCaieAHwULn0kKq","pdf",1047015,1,9,"English","en",105,"# What is Scientific Machine Learning?\n## Data-driven models\n## Physics-based models\n## Why scientific machine learning?\n# Scientific ML at the intersection of data-driven and physics-based models\n## Known vs Unknown Physics in the Geosciences\n## Potential applications\n## Landslide and rockfall prediction","[{\"question\":\"What is scientific machine learning in geotechnics and engineering geology?\",\"answer\":\"Scientific machine learning merges traditional data-driven machine learning with physics-based models so each compensates for the other's weaknesses. It leverages patterns from data while respecting governing physical principles.\"},{\"question\":\"What limitations affect data-driven and physics-based models?\",\"answer\":\"Data-driven models can be challenged by limited data availability and limited interpretability. Physics-based models may rely on assumptions that miss real-world behavior, face difficulties in parameter estimation, and can be computationally complex.\"},{\"question\":\"How can combining the two approaches improve prediction outcomes?\",\"answer\":\"Combining them can use data more efficiently, improve accuracy, and provide interpretability to predictions. It can also help capture aspects of reality that neither approach alone models well.\"}]","The Potential of Scientific Machine Learning in Geotechnics and Engineering Geology - Introduction and Applications | PDF",1785680307,23,{"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},"the-potential-of-scientific-machine-learning-in-geotechnics-and-engineering-geology-introduction-and-applications","",{"@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/the-potential-of-scientific-machine-learning-in-geotechnics-and-engineering-geology-introduction-and-applications/117905/",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","2026-08-02",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 is scientific machine learning in geotechnics and engineering geology?","Question",{"text":75,"@type":76},"Scientific machine learning merges traditional data-driven machine learning with physics-based models so each compensates for the other's weaknesses. It leverages patterns from data while respecting governing physical principles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations affect data-driven and physics-based models?",{"text":80,"@type":76},"Data-driven models can be challenged by limited data availability and limited interpretability. Physics-based models may rely on assumptions that miss real-world behavior, face difficulties in parameter estimation, and can be computationally complex.",{"name":82,"@type":73,"acceptedAnswer":83},"How can combining the two approaches improve prediction outcomes?",{"text":84,"@type":76},"Combining them can use data more efficiently, improve accuracy, and provide interpretability to predictions. 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