9 papers
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…
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…
Lagrangian-Eulerian learning of flow field and trajectories with TrajectoryFlowNet
Jingdi Wan, Hongping Wang, Bo Liu +5
Predicting particle transport in complex flows is traditionally achieved by solving the Navier-Stokes equations. While various numerical and experimental methods exist, they typica…
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…