WG4

Gaussian Process Regression — MecaNano Tutorial Series

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

Note: Download buttons have been disabled for the embedded documents to protect the tutorial content. Please contact the authors for access.

MecaNano Summer School 2025

The long awaited MecaNano Summer School 2025 is now fully organized to present new topics related to Mechanics at the Nanoscale! The summer school will be held 28. July to 1. August in Kassel, Germany home to the UNESCO mountain park and elected happiest city of Germany in 2024. The organizers (B. Merle, C. Kirchlechner, … Read more

2nd MecaNano Workshop on Machine Learning for Micro- and Nano-Mechanics

The 2nd MecaNano Workshop on Machine Learning for Micro- and Nano-Mechanics will take place on September 4–5, 2025, at ELTE Eötvös Loránd University, Faculty of Science, Budapest. Following the success of the first edition, this workshop continues to explore the integration of machine learning techniques in small-scale mechanical testing and materials science, with a focus … Read more

Coming Soon: WG4 MecaNano Tutorial Series

 

We are excited to announce two upcoming video tutorials on Machine Learning & AI in materials science, delivered by WG4 leader Prof. Stefan Sandfeld.

  • Tutorial 1: Introduction to Machine Learning for Materials Scientists
    A beginner-friendly overview of machine learning techniques, using practical examples tailored to materials science.
  • Tutorial 2: From Specialist Models to Generalist Models in Materials AI
    Exploring the transition from specialized models to broad, generalist models in materials science AI.

The first tutorial will be released in November 2024. Stay tuned and enjoy our teaser video while we prepare the hands-on content.

New book by MecaNano member on Machine Learning

MecaNano Member and WG4 co-lead, Stefan Sandfeld, has published a new book titled “Materials Data Science — Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering.” The book is aimed for Material Science students in their first Masters of Science semesters and PhD students or scientists who haven’t had much … Read more

First topical workshop on Machine Learning (WG4) – Feb. 22-23, 2024 – ABSTRACT SUBMISSION OPEN

The abstract submission is still open for our 2-day event related to the WG4 “Machine Learning” here: https://mecanano-wg4-24.sciencesconf.org/ The workshop will be held on 22-23 February 2024 in Louvain-la-Neuve, Belgium. (Note: this is a different city to Louvain!) The community is rather diverse and has a large range of experienced in the use of ML: … Read more