Machine Learning Fundamentals for Materials Science

This tutorial focuses on explainable machine learning for materials mechanics and nanomechanical testing. The session discusses why explainability is essential when machine-learning models are used to analyse experimental datasets, where the results must remain physically meaningful and scientifically defensible. It introduces 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.

Tutorial Slides

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1. Overview of Gaussian Processes

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This tutorial introduces the foundations of machine learning for nanoindentation and nanomechanical data analysis. The session covers the basic concepts of 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. It discusses how data-driven methods can support phase identification, detection of anomalous curves, interpretation of mechanical populations, and integration with correlative microstructural information.

Tutorial Slides

The following documents can be read directly from this page but are protected from downloading.

Slides Deck

This tutorial introduces Gaussian Process Regression (GPR), a non-parametric probabilistic approach widely adopted for interpolation, regression, and uncertainty quantification in materials science. The session explores both 1D and 2D inference, implemented with pyro.contrib.gp and PyTorch, with examples crafted for clarity and practical use.

Tutorial Slides

The following documents can be read directly from this page but are protected from downloading.

1. Overview of Gaussian Processes

2. Univariate GP Regression

3. Bivariate GP Regression

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