Machine-precision prediction achieved for chaotic systems

A new research paper demonstrates that low-dimensional chaotic systems can be predicted with machine precision from noise-free data. The study introduces a system-agnostic method using ordinary least squares regression with high-degree polynomial features and 512-bit arithmetic. This approach achieves prediction times significantly longer than previous methods, extending to 36 Lyapunov times for the Lorenz-63 system and even further with optimized precision. The findings were validated on more complex systems like Thomas' Cyclically Symmetric Attractor and the Lorenz-96 model, suggesting that forecasting such systems from clean data is now effectively a solved problem. AI

IMPACT This research suggests that forecasting low-dimensional chaotic systems from noise-free data is effectively a solved problem, potentially impacting scientific modeling and simulation.

RANK_REASON The cluster contains a research paper detailing a new method for predicting chaotic systems. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

• 512-bit arithmetic
• 64-bit numerical ODE solvers
• Christof Schötz
• Lorenz-63 model
• Lorenz-96 model
• Lyapunov times
• Thomas' cyclically symmetric attractor

• paper
• other

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