Characterization of anomalous diffusion through convolutional transformers

DOI10.1088/1751-8121/acafb3OpenAlexW4313528208MaRDI QIDQ5879067

J. Alberto Conejero, Nicolas Firbas, Miguel Ángel García-March, Òscar Garibo-I-Orts

Publication date: 24 February 2023

Published in: Journal of Physics A: Mathematical and Theoretical (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2210.04959

zbMATH Keywords

attentionanomalous diffusionrecurrent neural networksmachine learningconvolutional networkstransformers

Mathematics Subject Classification ID

Quantum theory (81-XX) Statistical mechanics, structure of matter (82-XX)

Related Items (1)

Preface: characterisation of physical processes from anomalous diffusion data

Uses Software

• Tensor2Tensor

Cites Work

• Unnamed Item
• Random diffusivity models for scaled Brownian motion
• Boosting the performance of anomalous diffusion classifiers with the proper choice of features
• Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
• Fractional Brownian Motions, Fractional Noises and Applications
• Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
• Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
• WaveNet-based deep neural networks for the characterization of anomalous diffusion (WADNet)

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