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
The following documents can be read directly from this page but are protected from downloading.
1. Overview of Gaussian Processes
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