The study of active matter offers deep insights into fundamental biological processes while opening new avenues for technological and medical innovation. By understanding the principles governing active matter, researchers can engineer materials with adaptive and responsive properties, develop more efficient and coordinated robotic systems, and design advanced approaches for drug delivery and tissue engineering.
In parallel, the growing role of machine learning in scientific research has transformed how complex systems are analyzed and understood. Originating in the mid-20th century with algorithms capable of learning from data, machine learning advanced rapidly with the emergence of powerful computing resources and large-scale datasets in the 1990s and 2000s. Foundational developments—such as backpropagation for training neural networks and the introduction of support vector machines—paved the way for modern approaches. More recently, breakthroughs in deep learning have dramatically enhanced pattern recognition and predictive capabilities, making machine learning an indispensable tool in contemporary science.
Within the field of active matter, machine learning provides powerful methods to analyze highly complex and often non-linear behaviors. By leveraging large volumes of experimental and simulation data, machine learning models can identify hidden patterns, predict system dynamics, and optimize the design and control of active matter systems. This integration bridges theoretical understanding and practical application, enabling the creation of synthetic active materials with tailored functionalities and advancing innovation across physics, engineering, and biomedical sciences.
TY - CHAPAU - Volpe, GiovanniPY - 2026/01/14SP - 217EP - 237SN - 978-3-032-04128-9T1 - Machine Learning for Active MatterVL - DO - 10.1007/978-3-032-04129-6_11ER -
For full paper: https://www.researchgate.net/publication/399730449_Machine_Learning_for_Active_Matter
