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The use of benchmarking is a common practice in the machine learning community to compare different models in a standardized setting. Synthetic datasets are used because they allow for a controlled environment and can be generated easily. However, it should be noted that synthetic data can never perfectly represent real-world data, and as such, every model should also be evaluated on real-world data before being used in critical applications.Potential risks associated with incorrect predictions of important systems such as weather and climate simulations or electromagnetic field simulations for safety assessment should be discussed thoroughly. Synthetic datasets can provide a useful starting point for model evaluation and the development of new approaches, but they need to be assessed on domain-specific data for real-world deployment. Particularly for safety-critical applications. While our proposed benchmark dataset and evaluated machine learning models provide useful insights into learning dynamical systems, they should not be used as the sole basis for making important political decisions, particularly concerning weather or climate data.While data-driven approaches have again and again shown their superiority over classical methods in a variety of applications, they are also prone to overfitting and adversarial attacks, if not carefully designed and validated. The risks and benefits of replacing existing numerical simulations or expert knowledge with deep learning approaches should always be taken into account and thoroughly discussed when developing and applying new models. 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