[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118576-en":3,"doc-seo-118576-105":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},118576,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Informed Machine Learning and Visual Analytics for Virtual Spinning Processes - Thesis & Visual Analytics","This doctoral thesis establishes how machine learning and visual analytics improve the industrial production of nonwovens, targeting quality enhancement and resource optimization through economical and sustainable workflows. It tackles limitations of numerical simulation models used as virtual twins of physical spinning processes. Using melt spinning as a key use case, the work addresses bottlenecks in virtual spinning simulation based on boundary value problem solvers. It introduces ML-driven BVP solver optimization, physics-informed acceleration for continuation problems, and visual analytics for interpretation, uncertainty quantification, and reliable neural-network usability.","Informed Machine Learning and Visual Analytics for Virtual Spinning Processes  \nThesis approved by The Department of Computer Science University of Kaiserslautern-Landau for the award of the Doctoral Degree Doctor of Engineering (Dr.-Ing.)  \nto  \nViny Saajan Victor  \nDate of Defense: 23 May 2025  \nDean: Prof. Dr. Christoph Garth  \nReviewer: Prof. Dr. Heike Leitte  \nReviewer: Prof. Dr. Simone Gramsch  \nDE-386  \nCurriculum Vitae  \nViny Saajan Victor is a PhD candidate at the University of Kaiserslautern-Landau and the Fraunhofer Institute for Industrial Mathematics in Germany. His research explores the use of reliable machine learning and visual analytics to enhance the manufacturing of technical textiles. The goal is to improve product quality and optimize resource usage by integrating domain-informed machine learning with classical numerical simulations. This approach aims to streamline and accelerate the optimization process, reducing both time and reliance on deep domain expertise.  \nEducation  \nSeptember 2011 – June 2015  \nOctober 2018 – March 2021  \nJune 2021 – current  \nBachelor of Engineering in Computer Science Visvesvaraya Technological University Belagavi, India  \nMasters in Computer Science University of Kaiserslautern-Landau Kaiserslautern, Germany  \nDoctoral Candidate  \nFraunhofer Institute for Industrial Mathematics Kaiserslautern, Germany  \nProfessional Experience  \nAugust 2015 – August 2018 Associate Software Engineer  \nRobert Bosch Engineering and Business Solutions Bengaluru, India  \nAbstract  \nThis doctoral thesis aims to establish the role of machine learning and visual analytics in the industrial production of nonwovens, aiming for quality and resource optimization through economical and sustainable production. To accomplish this, it addresses various challenges faced by numerical simulation models that act as virtual twins of physical processes. The thesis emphasizes the benefits of integrating engineering, numerical analysis, and artificial intelligence within an industrial application.  \nThe melt spinning process is selected as a key use case, being a primary and critical step in nonwoven production. In this context, the thesis investigates and addresses the bottlenecks encountered by a virtual spinning simulator, which relies on boundary value problem solvers to model the dynamics of the spinning process through a system of differential equations with boundary conditions. The thesis makes three main contributions. First, it introduces a machine learning pipeline to automate the optimization of BVP solver settings, enhancing convergence and computation speed. Second, it presents a physics-informed machine learning approach to accelerate BVP continuation problems by providing highquality solution approximations. Third, it integrates visual analytics to interpret simulation results, evaluate ML predictions, quantify uncertainty, and improve the interpretability of physics-informed neural networks—thereby fostering trust and usability.  \nThis thesis leverages the complementary capabilities of numerical simulations, machine learning, and visual analytics in the context of scientific machine learning to address complex industrial spinning processes. While melt spinning serves asa compelling and practical demonstration of these techniques, the methods are also applied to the simulation of fiber deposition in nonwoven manufacturing, showcasing their general applicability. The thesis concludes by discussing the broader industrial impact and future directions in ML-augmented simulations and human-centered visual analytics.  \nContents  \n1 Introduction 1  \n1.1 Application Domain: Spunbond Processes ............... 2  \n1.2 Numerical Simulation of Spunbond Processes ............. 4  \n1.2.1 Melt Spinning ............................ 6  \n1.2.2 Fiber Deposition and Virtual Nonwoven Production .... 7  \n1.2.3 Challenges in Numerical Simulations ............. 7  \n1.3 Role of Scientific Machine Learning ................... ","cbCaicvVHPtn9YE7","https://ap.wps.com/l/cbCaicvVHPtn9YE7","pdf",10826176,1,115,"English","en",105,"# Contents\n## 1 Introduction\n## 2 Tuning Boundary Value Problem Solvers with Machine Learning for Optimized Fiber Simulations\n## 3 Physics-Informed Machine Learning based Boundary Value Problem Solving for Accelerated Fiber Simulations","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"To define how machine learning and visual analytics support industrial nonwoven production by improving quality and optimizing resource usage through simulation-ML integration.\"},{\"question\":\"Why are boundary value problem (BVP) solvers central to the approach?\",\"answer\":\"The virtual spinning simulator models spinning dynamics via differential equations with boundary conditions, making BVP solver performance a key bottleneck to optimize and accelerate.\"},{\"question\":\"What are the thesis’ three main contributions?\",\"answer\":\"A machine learning pipeline to automate BVP solver setting optimization, a physics-informed ML approach to accelerate BVP continuation problems with high-quality approximations, and visual analytics to interpret results, assess ML predictions, quantify uncertainty, and improve interpretability and trust.\"}]","Informed Machine Learning and Visual Analytics for Virtual Spinning Processes - 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