The research delves into the challenge of extending the forecast horizon in autoregressive neural operators. It highlights instability issues that limit the effectiveness of existing methods, proposing a novel solution that includes dynamic filters generated through a frequency-adaptive MLP. Experimental results demonstrate significant stability improvements. The work showcases a groundbreaking stride in tackling forecast horizon extension challenges in autoregressive neural operators. [50 words]
Revolutionizing Autoregressive Neural Operators for Spatiotemporal Forecasting
Challenges and Proposed Solution
This research addresses the limitations of autoregressive neural operators in extending the forecast horizon. The proposed method introduces a fundamental architectural shift in spectral neural operators, enabling an indefinite forecast horizon, marking a substantial leap forward in spatiotemporal forecasting.
Key Innovation
The proposed method restructures the neural operator block, introducing dynamic filters to handle challenges like aliasing and discontinuity. This adaptability is achieved through a mode-wise multilayer perceptron (MLP) operating in the frequency domain, resulting in significant stability improvements and adaptability to diverse datasets.
Experimental Results
Experimental results underscore the efficacy of the method, revealing significant stability improvements, particularly evident in scenarios like the rotating shallow water equations and the ERA5 dataset. The dynamic filters, generated through the frequency-adaptive MLP, emerge as pivotal in ensuring the model’s adaptability to diverse datasets.
Conclusion
This research represents a groundbreaking stride in overcoming the persistent challenge of extending the forecast horizon in autoregressive neural operators. The restructuring of the neural operator block, characterized by incorporating dynamic filters generated through a frequency-adaptive MLP, is a highly effective strategy for mitigating instability issues and enabling an indefinite forecast horizon.
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