Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction
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 mitigates the vanishing gradients and eliminates the risk of numerical divergence in training seen in existing structures that feed back state estimat...
| Authors | Ruiyuan Li, Ajay Seth, Manon Kok |
|---|---|
| Primary category | eess.SY |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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