Stable Long-Horizon Inference: Blending Neural Operators and Traditional Solvers

This research explores a promising approach to improve the stability and performance of long-horizon inference in AI models. By hybridizing neural operators and solvers, the authors likely aim to leverage the strengths of both, potentially leading to more robust and reliable predictions over extended time periods.

• •Addresses the challenge of stable long-horizon inference in AI.
• •Combines neural operators and traditional solvers for improved performance.
• •Potentially leads to more reliable predictions over extended time frames.