52 citations · 141 across the 13 of their papers we have counts for
13 papers
Neural Partial Differential Equations with Functional Convolution
Ziqian Wu, Xingzhe He, Yijun Li +4
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs. Our key idea is to leverage the prior of…
Solid-Fluid Interaction with Surface-Tension-Dominant Contact
Liangwang Ruan, Jinyuan Liu, Bo Zhu +3
We propose a novel three-way coupling method to model the contact interaction between solid and fluid driven by strong surface tension. At the heart of our physical model is a thin…
Thin-Film Smoothed Particle Hydrodynamics Fluid
Mengdi Wang, Yitong Deng, Xiangxin Kong +3
We propose a particle-based method to simulate thin-film fluid that jointly facilitates aggressive surface deformation and vigorous tangential flows. We build our dynamics model fr…
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…
Sparse Symplectically Integrated Neural Networks
Daniel M. DiPietro, Shiying Xiong, Bo Zhu
We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data. SSINNs combine fourth-order symplectic i…
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…
Lagrangian-Eulerian Multi-Density Topology Optimization with the Material Point Method
Yue Li, Xuan Li, Minchen Li +3
In this paper, a hybrid Lagrangian-Eulerian topology optimization (LETO) method is proposed to solve the elastic force equilibrium with the Material Point Method (MPM). LETO transf…
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…
PuzzleFlex: kinematic motion of chains with loose joints
Samuel Lensgraf, Karim Itani, Yinan Zhang +7
This paper presents a method of computing free motions of a planar assembly of rigid bodies connected by loose joints. Joints are modeled using local distance constraints, which ar…