52 citations · 94 across the 18 of their papers we have counts for
7 papers · 1 filter
Nonseparable Symplectic Neural Networks
Shiying Xiong, Yunjin Tong, Xingzhe He +3
Predicting the behaviors of Hamiltonian systems has been drawing increasing attention in scientific machine learning. However, the vast majority of the literature was focused on pr…
Soft Multicopter Control using Neural Dynamics Identification
Yitong Deng, Yaorui Zhang, Xingzhe He +5
Dynamic control of a soft-body robot to deliver complex behaviors with low-dimensional actuation inputs is challenging. In this paper, we present a computational approach to automa…
RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data
Shiying Xiong, Xingzhe He, Yunjin Tong +2
We introduce Roe Neural Networks (RoeNets) that can predict the discontinuity of the hyperbolic conservation laws (HCLs) based on short-term discontinuous and even continuous train…
Learning Physical Constraints with Neural Projections
Shuqi Yang, Xingzhe He, Bo Zhu
We propose a new family of neural networks to predict the behaviors of physical systems by learning their underpinning constraints. A neural projection operator lies at the heart o…
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian Dynamics
Shiying Xiong, Xingzhe He, Yunjin Tong +2
In the field of fluid numerical analysis, there has been a long-standing problem: lacking of a rigorous mathematical tool to map from a continuous flow field to discrete vortex par…
Symplectic Neural Networks in Taylor Series Form for Hamiltonian Systems
Yunjin Tong, Shiying Xiong, Xingzhe He +2
We propose an effective and lightweight learning algorithm, Symplectic Taylor Neural Networks (Taylor-nets), to conduct continuous, long-term predictions of a complex Hamiltonian d…
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing
Xingzhe He, Helen Lu Cao, Bo Zhu
This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics. Our learning architecture…