Machine Learning for Active Matter
Abstract
The integration of machine learning, and particularly deep learning, with active matter research provides new possibilities for research and innovation. Active matter systems, which encompass a broad range of natural and artificial entities that consume energy to perform mechanical work, present unique challenges due to their intrinsic out-of-equilibrium dynamics. Recent advancements in deep learning, offer unprecedented opportunities to analyze, model, and understand these complex systems. By addressing both the opportunities and challenges, including the need for physics-informed models and the reality gap between simulations and real-world applications, this chapter highlights the mutual benefits of combining machine learning with active matter research.
Fields of Study & Subjects
Machine Learning in Materials Science
Micro and Nano Robotics
Quantum many-body systems
Publication details
- Journal / source
- Machine intelligence for materials science
- Publisher
- Springer Nature Switzerland
- Pages
- 217–237
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Book chapter