MecaNano Live tutorial series on Machine Learning for Micro- and Nano-mechanics

Event overview

This online tutorial day introduces the use of machine learning methods for the analysis, interpretation and exploitation of nanoindentation and nanomechanical datasets.

The 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.

The day includes two complementary live tutorials.

Tutorials

Tutorial 1. Machine learning bases and advanced applications for nanoindentation data analysis

Speaker: Prof. Edoardo Rossi, Università degli Studi Roma Tre

This 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.

The 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.

Tutorial 2. Explainable Machine Learning

Speaker: Dr. Claus Trost, Erich Schmid Institute of Materials Science, Austrian Academy of Sciences

This 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.

The 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.

Programme

All times are given in the Europe/Zurich timezone.

Each tutorial lasts two hours: 90 minutes of tutorial followed by 30 minutes of questions and discussion.

Target audience

The event is intended for PhD students, postdoctoral researchers and researchers working in nanoindentation, small-scale mechanical testing, materials characterization and data-driven materials science.

No advanced background in machine learning is required, although basic familiarity with nanoindentation data and scientific data analysis will be useful.

Learning outcomes

By the end of the tutorial day, participants should be able to:

• understand the basic logic of supervised and unsupervised machine learning methods;
• recognize how machine learning can be applied to nanoindentation curves and high-throughput indentation maps;
• identify suitable workflows for clustering, classification, anomaly detection and full-curve analysis;
• understand the importance of interpretability when applying machine learning to experimental nanomechanics;
• critically evaluate whether machine-learning outputs are physically meaningful and scientifically defensible.

Practical information

The tutorials will be held online. Connection details will be provided to registered participants before the event.

Participants are encouraged to attend both tutorials, as the sessions are complementary. Any required material or additional instructions will be communicated through the event page.