Characterizing anomalous diffusion is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. In this work, we characterize the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated with a bidirectional scan mechanism. Our implementation is tested on the AnDi-2 challenge datasets among others. Designed for regression tasks, the Bi-Mamba architecture infers efficiently the effective diffusion coefficient and anomalous exponent from single, short trajectories. As such, our results indicate the potential practical use of the Bi-Mamba architecture for anomalousdiffusion characterization.
@article{arxiv.2412.07299,
title = {Bidirectional Mamba state-space model for anomalous diffusion},
author = {Maxime Lavaud and Yosef Shokeeb and Juliette Lacherez and Yacine Amarouchene and Thomas Salez},
journal= {arXiv preprint arXiv:2412.07299},
year = {2024}
}