2nd MecaNano Workshop on Machine Learning for Micro- and Nano-Mechanics
MecaNano, which stands for "European Network for the Mechanics of Matter at the Nano-Scale", is a European Cooperation in Science & Technology (COST) Action running 2022-2026. The Action is intended as a broad international cooperation aiming to advance the multiscale understanding of the mechanical behavior of nanostructured materials. By combining the expertise of its participants – from experimentalists to simulation, data management and machine learning experts – it aims to overcome the different bottlenecks limiting the exploration of mechanical size effects. MecaNano provides its members with numerous opportunities to interact and collaborate, e.g. through dedicated workshops, symposia and summer schools, or by funding the mobility between participants.
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 on real-world applications and research challenges.
Scope
The workshop will feature invited talks from leading experts and foster discussions on how machine learning can enhance mechanical characterization, predictive modeling, and materials discovery. Topics will include, but are not limited to:
Machine learning for nanoindentation and mechanical characterization
Uncertainty quantification and data-driven modeling
Multiscale simulations in materials science
AI-driven materials discovery and design
This workshop is open to researchers at all levels, from those already applying machine learning to materials mechanics to those looking for new approaches to analyze experimental and simulation data. It will provide a platform for knowledge exchange, discussion of emerging methodologies, and exploration of new research directions at the interface of machine learning and micro- and nano-mechanics.