:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Brenner, Manuel, Koppe, Georgia
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation and Language Chaotic Dynamics Computational Physics
Online Access:	https://arxiv.org/abs/2506.07919
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction
by: Brenner, Manuel, et al.
Published: (2024)

Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
by: Brenner, Manuel, et al.
Published: (2024)

Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
by: Brenner, Manuel, et al.
Published: (2022)

Multiple Descents in Deep Learning as a Sequence of Order-Chaos Transitions
by: Wei, Wenbo, et al.
Published: (2025)

Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
by: Hemmer, Christoph Jürgen, et al.
Published: (2024)

Out-of-Domain Generalization in Dynamical Systems Reconstruction
by: Göring, Niclas, et al.
Published: (2024)

Correlation Dimension of Auto-Regressive Large Language Models
by: Du, Xin, et al.
Published: (2025)

Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
by: Norton, Declan A., et al.
Published: (2025)

Using Machine Learning and Neural Networks to Analyze and Predict Chaos in Multi-Pendulum and Chaotic Systems
by: Ramachandruni, Vasista, et al.
Published: (2025)

A scalable generative model for dynamical system reconstruction from neuroimaging data
by: Volkmann, Eric, et al.
Published: (2024)

Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
by: Liu, Wei, et al.
Published: (2025)

Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems
by: Vlachas, Pantelis R., et al.
Published: (2025)

Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge
by: Schmidt, Oliver T., et al.
Published: (2026)

Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing
by: Sedehi, Omid, et al.
Published: (2025)

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
by: Zhang, Yuanzhao, et al.
Published: (2025)

Learning finite symmetry groups of dynamical systems via equivariance detection
by: Calvo-Barlés, Pablo, et al.
Published: (2025)

GEN2: A Generative Prediction-Correction Framework for Long-time Emulations of Spatially-Resolved Climate Extremes
by: Wang, Mengze, et al.
Published: (2025)

Tailored Forecasting from Short Time Series via Meta-learning
by: Norton, Declan A., et al.
Published: (2025)

Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
by: Heidenreich, Hunter S., et al.
Published: (2024)

Generative learning for nonlinear dynamics
by: Gilpin, William
Published: (2023)

Zero-shot forecasting of chaotic systems
by: Zhang, Yuanzhao, et al.
Published: (2024)

Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)

Sparse identification of quasipotentials via a combined data-driven method
by: Lin, Bo, et al.
Published: (2024)

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
by: Hu, Jiaxi, et al.
Published: (2024)

ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics
by: Moges, Henok Tenaw, et al.
Published: (2026)

From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs
by: Lai, Pengyu, et al.
Published: (2026)

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
by: Asefi, Niloofar, et al.
Published: (2025)

LanTu: Dynamics-Enhanced Deep Learning for Eddy-Resolving Ocean Forecasting
by: Zheng, Qingyu, et al.
Published: (2025)

OceanNet: A principled neural operator-based digital twin for regional oceans
by: Chattopadhyay, Ashesh, et al.
Published: (2023)

True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics
by: Hemmer, Christoph Jürgen, et al.
Published: (2025)

Horizon-Constrained Rashomon Sets for Chaotic Forecasting
by: Kale, Gauri, et al.
Published: (2026)

Enhancing Computational Efficiency of Motor Imagery BCI Classification with Block-Toeplitz Augmented Covariance Matrices and Siegel Metric
by: Carrara, Igor, et al.
Published: (2024)

A Numerical Proof of Shell Model Turbulence Closure
by: Ortali, Giulio, et al.
Published: (2022)

Hierarchy of extreme-event predictability in turbulence revealed by machine learning
by: Yang, Yuxuan, et al.
Published: (2026)

Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators
by: Lupin-Jimenez, Leonard, et al.
Published: (2025)

Escaping Mode Collapse in LLM Generation via Geometric Regulation
by: Du, Xin, et al.
Published: (2026)

Vision-based Discovery of Nonlinear Dynamics for 3D Moving Target
by: Zhang, Zitong, et al.
Published: (2024)

Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data
by: Maksymov, Ivan S.
Published: (2024)

Attractor-merging Crises and Intermittency in Reservoir Computing
by: Kabayama, Tempei, et al.
Published: (2025)

Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime
by: Abdel-Ghani, Hend, et al.
Published: (2025)