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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Practical Implementation
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