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Efficient Long-Term Prediction of Chaotic Systems Using Physics-Informed Neural Operators: Overcoming Limitations of Traditional Closure Models

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Efficient Long-Term Prediction of Chaotic Systems Using Physics-Informed Neural Operators: Overcoming Limitations of Traditional Closure Models

Predicting Long-Term Behavior of Chaotic Systems

Practical Solutions and Value

Predicting the behavior of chaotic systems like climate models requires significant resources. Instead of fully-resolved simulations, using coarse grids with machine learning methods can improve accuracy. Physics-informed neural operators (PINO) eliminate the need for closure models, providing accurate estimates with faster speed and minimal errors.

Efficient Long-Term Prediction of Chaotic Systems Using Physics-Informed Neural Operators

Overcoming Traditional Closure Models

Researchers introduce PINO to accurately predict chaotic system statistics without closure models. PINO offers a 120x speedup with ~5% error, outperforming traditional methods. This approach has broad applications in climate modeling and beyond.

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