Data-driven Mechanics and Physics of Materials

Combining classical principles of mechanics and physics of solids with cutting-edge data science techniques has resulted in very accurate and efficient data-driven approaches. Data-driven models have a very high capacity to harness vast volumes of data generated in material science, engineering, and physics to uncover hidden patterns, relationships, and insights. During the last decade, the following have seen key developments:

Efficient and accurate surrogate modelling: By leveraging existing data and employing Machine Learning (ML), very accurate and remarkably efficient ... show more

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Probabilistic Machine Learning

Machine learning-based uncertainty quantification of failure predictions
R. Li, M. Miranda, S. Pinho

Data-Driven Mechanics and Physics of Materials 2025.
- 19
- read
Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks
N. Kovacs, I. Rocha, F. Meer, C. Furtado, P. Camanho

Data-Driven Mechanics and Physics of Materials 2025.
- 2
- read
A Data-Driven Computational Workflow For The Growth Of Porous Anodized Aluminum Oxide: Integration Of Surrogate Modeling And Uncertainty Quantification
T. Albaraghteh, C. Feiler, M. Zheludkevich, D. Höche

Data-Driven Mechanics and Physics of Materials 2025.
- 22
- read
Exploring Flow Matching for Bifurcation: A Preliminary Framework for Buckling in Mechanical Metamaterials
F. Hendriks, V. Menkovski, M. Doškár, M. Geers, O. Rokoš

Data-Driven Mechanics and Physics of Materials 2025.
- 12
- read

Surrogate Modelling

Learning from Physics and Data

Image-Based Deep-Learning Models and Tools

Machine-Learning-Assisted Material Design and Manufacturing

Keynote Talk

Plenary Talks

editors

Mirkhalaf, Mohsen 31
University of Gothenburg /...

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Probabilistic Machine Learning

Machine learning-based uncertainty quantification of failure predictions

Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks

A Data-Driven Computational Workflow For The Growth Of Porous Anodized Aluminum Oxide: Integration Of Surrogate Modeling And Uncertainty Quantification

Exploring Flow Matching for Bifurcation: A Preliminary Framework for Buckling in Mechanical Metamaterials

Surrogate Modelling

Learning from Physics and Data

Image-Based Deep-Learning Models and Tools

Machine-Learning-Assisted Material Design and Manufacturing

Keynote Talk

Plenary Talks

editors

Mirkhalaf, Mohsen 31

Editorial board

information

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