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Learning Provable Neural Network Observer for Uncertain Dynamical Systems
Zhangyi Wang, Jiaxu Liu, Song Chen +3
In many safety-critical applications, control of uncertain dynamical systems relies on observers that estimate states and external disturbances. Neural network observers can improv…
DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling
Yixiao Qian, Song Chen, Pengkai Wang +3
Modern language models are built primarily from Transformers, recurrent models, and their hybrid architectures. Transformers rely on token-level attention memories, while recurrent…
DSSMs: State Space Models with Explicit Memory via Delay Differential Equations
Yixiao Qian, Song Chen, Jiaxu Liu +2
State Space Models (SSMs) have emerged as a powerful paradigm for efficient long-sequence modeling, offering parallel training and fast linear-time recurrent inference. However, li…
Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
Yixiao Qian, Jiaxu Liu, Zewei Xia +3
Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equ…