9 papers
Planning Oriented 3D Scene Completion via Coupled TUDF Occupancy Representation Learning from Partial Observations
Tianyou Yu, Pengfei Zhao, Chao Xu
Partial observability remains a fundamental challenge in robotic navigation, where limited sensor coverage and occlusions leave large portions of the environment unobserved. Existi…
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…
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…
A Continuous-Time Analysis of Smoothed Matrix-Polar Spectral Gradient Flows for Muon-Type Optimization
Jinlin Liu, Song Chen, Jiaxu Liu +1
This paper studies smoothed matrix-polar spectral gradient flows for unconstrained matrix-valued optimization.The canonical polar-factor map loses smoothness at rank-deficient matr…
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…
A Backstepping Framework for Unconstrained Accelerated Optimization Algorithms
Song Chen, Jiaxu Liu, Chao Xu
This paper introduces a control-theoretic perspective on unconstrained optimization algorithms using the backstepping methods. We model the optimization process as an augmented str…
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…
An Efficient Graph-Transformer Operator for Learning Physical Dynamics with Manifolds Embedding
Pengwei Liu, Xingyu Ren, Pengkai Wang +6
Accurate and efficient physical simulations are essential in science and engineering, yet traditional numerical solvers face significant challenges in computational cost when handl…
PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
Siming Shan, Pengkai Wang, Song Chen +3
The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations. Yet,…