Machine Learning in Industry and Science

Erscheinungsjahr 2027
ISBN: 978-3-032-39579-5
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Mathematics in Industry

Applied Machine Learning is transforming industries and public services across Europe. This book offers a unique insight into the field, bringing together work from researchers within the Fraunhofer network – one of the leading organizations for applied research and innovation.
Covering cutting-edge approaches to data mining, complex spatial data analysis, and knowledge modeling, the text reflects both theoretical advances and practical implementations. The contributions span a wide range of real-world domains, including healthcare, manufacturing, energy systems, and public administration. A particular focus is also placed on critical cross-cutting issues such as algorithmic transparency and the interpretability of AI systems.
What sets this volume apart is its emphasis on the strong connection between research excellence and application relevance. Drawing from the Fraunhofer ecosystem, it showcases how Machine Learning is shaping next-generation technologies and decision-making processes in both industry and public services.
This open access book is an essential resource for researchers, data scientists, AI practitioners, and policy stakeholders seeking insight into the current state and future direction of applied Machine Learning in Europe.

Zielgruppe

Research

Autoren/Hrsg.

Pfaff-Kastner, Manja Mai-Ly

Franke, Jürgen

Michel, Isabel

Kirch, Claudia

Kneiske, Tanja Manuela

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Part 1: Innovative Data Mining for Applications in Science, Medicine, and Healthcare.- Study on Text Classification for Public Administration.- AI-based analysis of slow time radar data for the identification of signs of life.- Sector-coupled cluster regions as basis for an energy-integrated placement of future power-to-x systems in Germany.- Changepoint Detection for Series of Curve Data.- Multiple Changepoint Analysis for Time Series with Neural Networks.- Part 2: Machine Learning for Modeling and Analyzing Complex Spatial Data.- Machine Learning Optimized Approach for Configuration Selection in MESHFREE Simulations.- Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems.- Applications Of Artificial Intelligence To Support Electronic System Development.- Part 3: Knowledge Discovery in Industrial Production and Transparency of AI.- Semantic modelling and simulation of the hydrogen process chain.- Structured ontology-based derivation of causal graphs for production with an application.- Explainable AI for Automatic Material Detection.

Über Autor(innen)

Dr. Manja Mai-Ly Pfaff-Kastner is a research associate at the Fraunhofer Institute for Machine Tools and Forming Technology IWU, where she leads the research group »Knowledge Models and Assistance Systems« and serves as deputy head of the department »Digitalization in Production«. Her research focuses on the development and application of ontologies for structuring domain knowledge across various industrial contexts. She has contributed to interdisciplinary projects in the fields of manufacturing, hydrogen technologies, and the construction sector. As part of her doctoral research, she developed a novel framework for ontology-based construction of causal graphs to support the digitalization of expert knowledge in production systems.

Dr. Pfaff-Kastner initiated the working group on Machine Learning and simulation that ultimately led to this book project. While her participation in the TALENTA program has formally concluded, she remains actively engaged in promoting the visibility of women in science. She is one of the initiators and editors of the publication »Forscherinnen im Fokus – Wir schaffen Veränderung« (Female Researchers in Focus – We Are Creating Change), which presents 42 female scientists. Each double-page profile highlights not only their research topics in an accessible and engaging way but also their unique journeys and perspectives as women in science, showcasing relatable role models for young women pursuing careers in science.
Prof. Dr. Jürgen Franke is professor for statistics at the University of Kaiserslautern. He held the chair of Applied Mathematical Statistics, and for several years he led, as the speaker, the research training group Mathematics and Practice and the interdisciplinary Center for Mathematical and Computational Modelling (CM)². As early as the end of the 1990s, he initiated several projects with industry, applying statistical machine learning to problems from finance and manufacturing.
After his retirement from teaching, he joined the Fraunhofer Institute for Industrial Mathematics ITWM as a scientific consultant in statistics. His research focuses on nonlinear time series analysis, nonparametric statistics and statistical learning with applications in engineering and finance.
Dr. Isabel Michel has been part of the Fraunhofer Institute for Industrial Mathematics ITWM for over fifteen years and is deputy head of the department »Grid-Free Methods«. Together with her colleague Dr. Jörg Kuhnert, she was awarded the Joseph von Fraunhofer Prize in 2024 for the development of the innovative simulation tool MESHFREE. With the help of the implemented Generalized Finite Difference Method, complex applications in fluid dynamics and continuum mechanics are simulated with great time savings and thus cost-effectively – from automotive to process engineering and beyond.
From 2017 to 2019, she was a participant in the Fraunhofer TALENTA program for the promotion of female scientists and was subsequently involved in the working group on Machine Learning and simulation. Dr. Isabel Michel is also one of the authors of the book »Forscherinnen im Fokus – Wir schaffen Veränderung« (Female Researchers in Focus – We Are Creating Change), which presents the world of Artificial Intelligence and simulation through the eyes of extraordinary female scientists and tells exciting, very personal stories in the process.
Since 2020, she has been an elected representative of the Scientific and Technical Council STC at Fraunhofer ITWM, which represents the interests of scientific employees to the Executive Board of the Fraunhofer-Gesellschaft.