arXiv:2606.02278v1 Announce Type: cross Abstract: State-space models are traditionally based on physical knowledge, but multi-step predictions from these physical models can be poor due to model inaccuracy. Black-box deep learning has shown promise as an alternative. However, these methods rely on the availability of large datasets and potentially available physical knowledge is neglected. We propose the PG-RSSNN, a physics-guided recurrent state-space neural network that incorporates recurrent structures to enable the use of non-saturating activation functions in multi-step prediction. It mit
The proliferation of deep learning for complex systems is driving a need to integrate existing physical knowledge with AI models to improve accuracy and efficiency without relying solely on vast datasets.
This development suggests a more robust and data-efficient method for AI predictions, potentially accelerating the development and deployment of AI in critical infrastructure and scientific domains where data is scarce or physical laws are well-understood.
AI models for multi-step prediction can now more effectively combine the strength of physical models with the adaptability of deep learning, leading to more accurate and reliable long-term forecasting.
- · AI/ML researchers
- · Engineering sectors
- · Scientific research
- · Industries with complex predictive needs
- · Purely black-box deep learning approaches
- · Traditional physics-only modeling
Improved accuracy and data efficiency in AI-driven multi-step predictions for various complex systems.
Reduced need for extremely large datasets in some AI applications, making AI more accessible and practical for data-scarce domains.
Accelerated development of AI for critical infrastructure, smart cities, and advanced scientific simulation, potentially leading to more resilient and optimized systems.
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