arXiv:2607. 11272v1 Announce Type: cross Abstract: Accurate dengue forecasting is crucial for public health planning, but remains challenging because incidence series are often short, noisy, non-stationary, nonlinear, and often affected by long-range temporal dependence.
By Rahul Goswami, Shinjini Paul, Palash Ghosh, Tanujit Chakraborty
arXiv:2505. 23863v3 Announce Type: replace-cross Abstract: Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and intervention in such systems are often infeasible.
By Chang Liu, Bohao Zhao, Jingtao Ding, Huandong Wang, Yong Li
arXiv:2607. 24420v1 Announce Type: cross Abstract: Reservoir computing has emerged as an efficient machine learning framework for predicting time series generated by dynamical systems.
By Arthur S Powanwe
arXiv:2609.24754v1 Announce Type: new
Abstract: We investigate next generation reservoir computing (NGRC) as a data-driven approach for inferring unseen components of dynamical systems. We compare NG...
By Jule Budnick, Andrew Keane, Serhiy Yanchuk
The study explores non‑smooth activation functions—chaotic, stochastic, and fractal—in echo state networks, testing 36,610 reservoir configurations. It finds that functions like the Cantor function preserve the Echo State Property (ESP) even at spectral radii up to 10, outperforming traditional smooth activations in convergence speed. The authors introduce a theoretical framework for quantized activations, defining a Degenerate Echo State Property (d‑ESP) that implies the traditional ESP, and show that preprocessing topology, rather than continuity, governs stability.
By Rae Chipera, Jenny Du, Irene Tsapara
arXiv:2511. 08860v2 Announce Type: replace-cross Abstract: The deep learning revolution has spurred a rise in advances of using AI in sciences.
By Zakhar Shumaylov, Peter Zaika, Philipp Scholl, Gitta Kutyniok, Lior Horesh, Carola-Bibiane Sch\"onlieb