arXiv AI June 10, 2026 By Shundong Li

Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark

arXiv:2606. 10084v1 Announce Type: cross Abstract: This work presents a divide-and-conquer modeling strategy for the CTF-4-Science Lorenz benchmark, which evaluates chaotic-system prediction across twelve hidden scores and five scenario families: clean forecasting, noisy reconstruction, noisy-input forecasting, few-shot learning, and parametric generalization.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv AI

Jun 4

Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge

arXiv:2606. 04191v1 Announce Type: cross Abstract: We describe our approach to the CTF4Science Lorenz challenge, a benchmark that mixes short-horizon forecasting, long-time distribution matching, and trajectory reconstruction across nine task pairs.

By Cen Lu

benchmarks

arXiv Machine Learning

Aug 4

Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data

arXiv:2507. 09652v2 Announce Type: replace-cross Abstract: Low-dimensional chaotic systems such as the Lorenz-63 model are commonly used to benchmark system-agnostic methods for learning dynamics from data.

By Christof Sch\"otz, Niklas Boers

benchmarks

arXiv Machine Learning

Jul 17

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

arXiv:2511. 06609v4 Announce Type: replace Abstract: The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and engineering disciplines.

By Xuyang Li, John Harlim, Dibyajyoti Chakraborty, Romit Maulik

benchmarks

arXiv Machine Learning

Jun 30

Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations

arXiv:2605. 22242v3 Announce Type: replace Abstract: Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the underlying physical processes.

By Birgit K\"uhbacher, Daan Crommelin, Niki Kilbertus

arXiv AI

1d ago

PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models

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

rag

arXiv AI

Jul 16

TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling

arXiv:2607. 13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions.

By Songru Yang, Zili Liu, Tao Han, Ben Fei, Fenghua Ling, Lei Bai, Chang Liu, Xiangyang Ji, Zhenwei Shi, Zhengxia Zou

benchmarks

Related stories

Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge

Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations

PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models

TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling