no code implementations • 2 Sep 2023 • Òscar Garibo-i-Orts, Nicolás Firbas, Laura Sebastiá, J. Alberto Conejero
Here we present a new data-driven method for working with diffusive trajectories.
no code implementations • 10 Oct 2022 • Nicolás Firbas, Òscar Garibo-i-Orts, Miguel Ángel Garcia-March, J. Alberto Conejero
The results of the Anomalous Diffusion Challenge (AnDi Challenge) have shown that machine learning methods can outperform classical statistical methodology at the characterization of anomalous diffusion in both the inference of the anomalous diffusion exponent alpha associated with each trajectory (Task 1), and the determination of the underlying diffusive regime which produced such trajectories (Task 2).