Papers

• An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modeling (2026)
  Anif N. Shikder et al.
  1.78
• Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder (2026)
  Kewei Zhu et al.
  1.72
• Learning Stochastic Thermodynamics Directly from Correlation and Trajectory-Fluctuation Currents (2025)
  Jinghao Lyu and Kyle J. Ray and James P. Crutchfield
  1.11
• Machine learning identifies nullclines in oscillatory dynamical systems (2025)
  Bartosz Prokop et al.
  1.06
• Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures (2025)
  T\^am Johan Nguy\^en et al.
  1.06
• Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion (2022)
  Ziyan Zhang et al.
  —
• Digital twins of nonlinear dynamical systems (2022)
  Ling-Wei Kong et al.
  —
• Catch-22s of reservoir computing (2022)
  Yuanzhao Zhang and Sean P. Cornelius
  —
• Reconstruction, forecasting, and stability of chaotic dynamics from partial data (2023)
  Elise \"Ozalp and Georgios Margazoglou and Luca Magri
  —
• Persistent learning signals and working memory without continuous attractors (2023)
  Il Memming Park and \'Abel S\'agodi and Piotr Aleksander Sok\'o\l
  —
• Learning noise-induced transitions by multi-scaling reservoir computing (2023)
  Zequn Lin et al.
  —
• Learning spatio-temporal patterns with Neural Cellular Automata (2023)
  Alex D. Richardson et al.
  —
• Fold Bifurcation Identification through Scientific Machine Learning (2023)
  Giuseppe Habib and \'Ad\'am Horv\'ath
  —
• Improving the Performance of Echo State Networks Through State Feedback (2023)
  Peter J. Ehlers et al.
  —
• Forecasting the Forced van der Pol Equation with Frequent Phase Shifts Using Reservoir Computing (2024)
  Sho Kuno et al.
  —
• Adaptive control of recurrent neural networks using conceptors (2024)
  Guillaume Pourcel et al.
  —