Final Program

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Machine Learning for Mechanical Characterization
Advancing Nanoindentation Data Analysis towards Explainable Machine Learning and Open Science
Predicting Plastic Deformation in Crystalline Materials using Acoustic Emission
Machine learning-based X-ray diffraction line profile analysis for the characterization of compositional libraries of high-entropy alloys
Break
Materials discovery through machine learning
Machine Learning-Based Prediction of Mechanical Properties for Additively Manufactured Mo-Re Alloys
Characterization of the Intrinsic Stress Field of High Entropy Alloys Based on Machine Learning
Image Analysis and Computer Vision in Materials Mechanics
Machine Learning for Grain-by-Grain Multi-Phase Steel Identification using an Objective Ground Truth
Crack tracking for cyclic crack growth in tungsten fine wires
Determining the material porosity using the Python programming language
Data Integration and Multimodal Analysis
Machine Learning Strategies for Micromechanical Phase Classification from Nanoindentation Data
Correlative Microscopy and Nanoindentation: Machine Learning Clustering Methods to Quantify Individual Constituent Properties
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