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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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

Sep 24

Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics

arXiv:2609.27877v1 Announce Type: cross Abstract: Extreme events (EEs) in chaotic dynamics are rare broad excursions whose forecastability can be altered by dynamical noise. We investigate how noise...

By Andrei Velichko, Viet-Thanh Pham

agents

arXiv Machine Learning

Sep 1

Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Series Forecasting under Short-term Observations

arXiv:2608.29579v1 Announce Type: new Abstract: Chaotic time series forecasting is a challenging task due to its sensitivity to initial conditions and long-term unpredictability. Traditional methods...

By Yuhang Yao, Bohan Jiang

llms fine-tuning benchmarks

arXiv AI

Sep 10

Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series

The paper introduces a data‑adaptive nonlinear vector autoregression (NVAR) model that replaces fixed polynomial or random feature maps with a shallow, trainable multilayer perceptron (MLP). By jointly training the MLP and a linear readout via gradient‑based optimization, the model learns data‑driven nonlinearities while maintaining a simple readout structure, improving scalability in high‑dimensional settings. Experiments on several chaotic systems, both noise‑free and synthetically noisy, show that this adaptive NVAR outperforms standard NVAR, a leaky echo state network (ESN), and a hybrid ESN in predictive accuracy, demonstrating robust forecasting under noisy conditions.

By Sherkhon Azimov, Susana Lopez-Moreno, Eric Dolores-Cuenca, Sieun Lee, Jae-Il Kwon, Sangil Kim

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

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Related stories

Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge

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

Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics

Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Series Forecasting under Short-term Observations

Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series

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