Neural State Space Model

Neural state-space models (NSSMs) are a powerful class of deep learning models designed to represent and predict the behavior of dynamic systems from data. Current research focuses on improving the efficiency and accuracy of NSSMs, particularly through the use of architectures like recurrent neural networks (RNNs, such as LSTMs) and techniques such as model predictive control (MPC) and meta-learning for faster adaptation and improved generalization across similar systems. These advancements are enabling applications in diverse fields, including autonomous vehicle control, speech processing, and physiological signal analysis, where accurate modeling of complex, nonlinear dynamics is crucial. Furthermore, ongoing work addresses challenges like uncertainty quantification and efficient handling of irregularly sampled or missing data.