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The paper introduces the Koopman neural operator (KNO) as a novel mesh-free solver for non-linear partial differential equations (PDEs), addressing the limitations of existing neural operator models in learning long-term dynamics. By approximating the Koopman operator, the KNO effectively learns solutions to a family of PDEs through simple linear prediction problems, demonstrating superior performance in various applications such as turbulence modeling and precipitation forecasting. Validation experiments o
- Author
- jamjamsanta
- Language
- EN