Preface: characterisation of physical processes from anomalous diffusion data
From MaRDI portal
Publication:5879064
DOI10.1088/1751-8121/acb1e1OpenAlexW4317435829MaRDI QIDQ5879064
No author found.
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/2301.00800
Conference proceedings and collections of articles (00Bxx) Quantum theory (81-XX) Statistical mechanics, structure of matter (82-XX)
Related Items (1)
Cites Work
- Bayesian inference of scaled versus fractional Brownian motion
- Anomalous diffusion originated by two Markovian hopping-trap mechanisms
- Characterization of anomalous diffusion in one-dimensional quantum walks
- Boosting the performance of anomalous diffusion classifiers with the proper choice of features
- Tempered fractionally integrated process with stable noise as a transient anomalous diffusion model
- Unsupervised learning of anomalous diffusion data: an anomaly detection approach
- Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
- Decomposing the effect of anomalous diffusion enables direct calculation of the Hurst exponent and model classification for single random paths
- Classification of stochastic processes by convolutional neural networks
- Classification of anomalous diffusion in animal movement data using power spectral analysis
- Fractional Brownian motion with random diffusivity: emerging residual nonergodicity below the correlation time
- Limit properties of Lévy walks
- Brownian non-Gaussian diffusion of self-avoiding walks
- Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
- Learning physical properties of anomalous random walks using graph neural networks
- Empirical anomaly measure for finite-variance processes
- Integrable fractional modified Korteweg–deVries, sine-Gordon, and sinh-Gordon equations
- Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
- Extreme learning machine for the characterization of anomalous diffusion from single trajectories (AnDi-ELM)
- WaveNet-based deep neural networks for the characterization of anomalous diffusion (WADNet)
- Bayesian inference of Lévy walks via hidden Markov models
- Characterization of anomalous diffusion through convolutional transformers
This page was built for publication: Preface: characterisation of physical processes from anomalous diffusion data