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X-WR-CALDESC:Events for MecaNano
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DTSTART:20250101T000000
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DTSTART;TZID=UTC:20260713T094500
DTEND;TZID=UTC:20260713T163000
DTSTAMP:20260703T023505
CREATED:20260623T154110Z
LAST-MODIFIED:20260623T154918Z
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SUMMARY:MecaNano Live tutorial series on Machine Learning for Micro- and Nano-mechanics
DESCRIPTION:Event overview\nThis online tutorial day introduces the use of machine learning methods for the analysis\, interpretation and exploitation of nanoindentation and nanomechanical datasets. \nThe event is designed as an interactive training activity\, combining conceptual explanations with examples relevant to nanoindentation curves\, high-throughput indentation maps\, data-driven materials characterization and physically meaningful model interpretation. \nThe day includes two complementary live tutorials. \nTutorials\nTutorial 1. Machine learning bases and advanced applications for nanoindentation data analysis\nSpeaker: Prof. Edoardo Rossi\, Università degli Studi Roma Tre \nThis tutorial introduces the foundations of machine learning for nanoindentation and nanomechanical data analysis. The session covers supervised and unsupervised learning\, feature extraction from indentation curves\, clustering and classification of indentation datasets\, analysis of high-throughput nanoindentation maps\, and advanced workflows based on the full load-displacement curve. \nThe tutorial will discuss how data-driven methods can support phase identification\, detection of anomalous curves\, interpretation of mechanical populations and integration with correlative microstructural information. \nTutorial 2. Explainable Machine Learning\nSpeaker: Dr. Claus Trost\, Erich Schmid Institute of Materials Science\, Austrian Academy of Sciences \nThis tutorial focuses on explainable machine learning for materials mechanics and nanomechanical testing. The session will discuss why explainability is essential when machine-learning models are used to analyse experimental datasets\, where the results must remain physically meaningful and scientifically defensible. \nThe tutorial will introduce strategies to understand model decisions\, identify relevant features\, evaluate model reliability and avoid black-box conclusions that cannot be connected to the underlying material behaviour or experimental conditions. \nProgramme\nAll times are given in the Europe/Zurich timezone. \n\n\n\nTime\nActivity\n\n\n09:45 to 10:00\nWelcome and introduction to the MecaNano tutorial day\n\n\n10:00 to 11:30\nTutorial 1: Machine learning bases and advanced applications for nanoindentation data analysis\n\n\n11:30 to 12:00\nQuestions and discussion\n\n\n12:00 to 14:00\nLunch break\n\n\n14:00 to 15:30\nTutorial 2: Explainable Machine Learning\n\n\n15:30 to 16:00\nQuestions and discussion\n\n\n16:00 to 16:10\nClosing remarks\n\n\n\nEach tutorial lasts two hours: 90 minutes of tutorial followed by 30 minutes of questions and discussion. \nTarget audience\nThe event is intended for PhD students\, postdoctoral researchers and researchers working in nanoindentation\, small-scale mechanical testing\, materials characterization and data-driven materials science. \nNo advanced background in machine learning is required\, although basic familiarity with nanoindentation data and scientific data analysis will be useful. \nLearning outcomes\nBy the end of the tutorial day\, participants should be able to: \n\nunderstand the basic logic of supervised and unsupervised machine learning methods;\nrecognize how machine learning can be applied to nanoindentation curves and high-throughput indentation maps;\nidentify suitable workflows for clustering\, classification\, anomaly detection and full-curve analysis;\nunderstand the importance of interpretability when applying machine learning to experimental nanomechanics;\ncritically evaluate whether machine-learning outputs are physically meaningful and scientifically defensible.\n\nPractical information\nThe tutorials will be held online. Connection details will be provided to registered participants before the event. \nParticipants are encouraged to attend both tutorials\, as the sessions are complementary. Any required material or additional instructions will be communicated through the event page.
URL:https://mecanano.com/event/mecanano-live-tutorial-series-on-machine-learning-for-micro-and-nano-mechanics/
LOCATION:Online Conference
CATEGORIES:Workshop
ORGANIZER;CN="Claus Trost":MAILTO:Claus.Trost@oeaw.ac.at
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