1.2k citations · 2k across the 19 of their papers we have counts for
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Trajectory-Level Mode Guidance for Controllable Diffusion-Based Multi-Robot Motion Planning
Tianyou Yu, Shengze Cai, Chao Xu
Motion planning often admits multiple feasible solutions, making multimodal generation valuable, particularly for flexible multi-robot coordination. Diffusion models naturally lear…
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…
Acoustic-driven millimetric helical robot: ultrasonic synergistic manipulation in confined fluidic environment
Hanlin Wang, Xin Wang, Xinwei Wei +4
Acoustic field-driven manipulation provides a non-contact and non-invasive strategy for controlling microscale and nanoscale objects, yet its extension to millimeter-scale robots w…
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…
Transformer-based Neural Operators for 3D Wind Field Prediction over Complex Mountainous Terrain
Yujia Zhang, Jiaxi Qi, Ruiyan Chen +5
Accurate prediction of three-dimensional (3D) wind fields over complex mountainous terrain is essential for renewable energy deployment and regional weather modeling. Traditional c…
Deep learning accelerated solutions of incompressible Navier-Stokes equations on non-uniform Cartesian grids
Heming Bai, Dong Zhang, Shengze Cai +1
In incompressible flow simulations, non-uniform grids efficiently capture localized flow features; however, their spatially varying resolutions severely exacerbate computational co…
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…