Anomalous Diffusion

Anomalous diffusion describes deviations from standard Brownian motion, where particle movement isn't solely dictated by random walks. Current research focuses on developing robust methods for classifying and characterizing different types of anomalous diffusion, employing machine learning techniques such as neural networks (including convolutional transformers and recurrent networks), and image-based representations like Gramian Angular Fields to analyze trajectory data. These advancements improve the accuracy and efficiency of identifying underlying diffusive regimes and inferring key parameters, with significant implications for diverse fields including physics, biology, and cybersecurity, where understanding complex transport processes is crucial. The development of interpretable machine learning models and the creation of publicly available datasets are also key areas of focus.