activity
20192021
most citedSolid-Fluid Interaction with Surface-Tension-Dominant Contact

19 citations · 26 across the 3 of their papers we have counts for

collaborators

9 papers

physics.flu-dyn202119 cited

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…

physics.flu-dyn20212 cited

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…

cs.RO2020

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…

physics.comp-ph20205 cited

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…

cs.LG2020

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…

cs.NE2020

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…