BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:MecaNano Live tutorial series on Machine Learning for Micro- and N ano-mechanics DTSTART:20260713T074500Z DTEND:20260713T141000Z DTSTAMP:20260804T181800Z UID:indico-event-18363@indico.global CONTACT:edoardo.rossi@uniroma3.it\;Claus.Trost@oeaw.ac.at DESCRIPTION:Speakers: Edoardo Rossi (Università degli Studi Roma Tre)\, C laus Trost (Erich Schmid Institute of Materials Science of the Austrian Ac ademy of Sciences)\n\nMecaNano is the COST Action CA21121 "European Networ k for the Mechanics of Matter at the Nano-Scale"\, focused on nanoscale me chanics\, nanomechanical testing\, data interoperability\, training\, and the integration of experimental\, modelling\, and machine-learning approac hes.\nThis online tutorial day introduces participants to the use of machi ne learning methods for the analysis\, interpretation\, and exploitation o f nanoindentation and nanomechanical datasets. The event is designed as an interactive training activity\, combining conceptual explanations with ex amples relevant to nanoindentation curves\, high-throughput indentation ma ps\, data-driven materials characterization\, and physically meaningful mo del interpretation. The day includes two complementary tutorials.\nTutoria ls\nTutorial 1. Machine learning bases and advanced applications for nanoi ndentation data analysisEdoardo Rossi\nThis tutorial introduces the founda tions of machine learning for nanoindentation and nanomechanical data anal ysis. The session covers the basic concepts of supervised and unsupervised learning\, feature extraction from indentation curves\, clustering and cl assification of indentation datasets\, analysis of high-throughput nanoind entation maps\, and advanced workflows based on the full load-displacement curve. It discusses how data-driven methods can support phase identificat ion\, detection of anomalous curves\, interpretation of mechanical populat ions\, and integration with correlative microstructural information.\nTuto rial 2. Explainable Machine LearningClaus Trost\nThis tutorial focuses on explainable machine learning for materials mechanics and nanomechanical te sting. The session discusses why explainability is essential when machine- learning models are used to analyse experimental datasets\, where the resu lts must remain physically meaningful and scientifically defensible. It in troduces strategies to understand model decisions\, identify relevant feat ures\, evaluate model reliability\, and avoid black-box conclusions that c annot be connected to the underlying material behaviour or experimental co nditions.\nProgramme\nAll times are given in the Europe/Zurich timezone. E ach tutorial lasts two hours: 90 minutes of tutorial followed by 30 minute s of questions and discussion.\n\n\n\n09:45 - 10:00\nWelcome and introduct ion to the MecaNano tutorial day\n\n\n10:00 - 11:30\nTutorial 1: Machine l earning bases and advanced applications for nanoindentation data analysis (Edoardo Rossi)\n\n\n11:30 - 12:00\nQuestions and discussion\n\n\n12:00 - 14:00\nLunch break\n\n\n14:00 - 15:30\nTutorial 2: Explainable Machine Lea rning (Claus Trost)\n\n\n15:30 - 16:00\nQuestions and discussion\n\n\n16:0 0 - 16:10\nClosing remarks\n\n\n\nTarget audience\nThe event is intended f or PhD students\, postdoctoral researchers\, and researchers working in na noindentation\, small-scale mechanical testing\, materials characterizatio n\, and data-driven materials science. No advanced background in machine l earning is required\, although basic familiarity with nanoindentation data and scientific data analysis will be useful.\nLearning outcomes\nBy the e nd of the tutorial day\, participants should be able to:\n\nUnderstand the basic logic of supervised and unsupervised machine learning methods.\nRec ognize how machine learning can be applied to nanoindentation curves and h igh-throughput indentation maps.\nIdentify suitable workflows for clusteri ng\, classification\, anomaly detection\, and full-curve analysis.\nUnders tand the importance of interpretability when applying machine learning to experimental nanomechanics.\nCritically evaluate whether machine-learning outputs are physically meaningful and scientifically defensible.\n\nPracti cal information\nThe tutorials will be held online. Connection details wil l be provided to registered participants before the event. Participants ar e encouraged to attend both tutorials\, as the sessions are complementary. Any required material or additional instructions will be communicated thr ough the event page.\n\nhttps://indico.global/event/18363/ LOCATION:Online URL:https://indico.global/event/18363/ END:VEVENT END:VCALENDAR