52 citations · 114 across the 9 of their papers we have counts for
10 papers
WorldAgent: Verification-Guided Agentic Physical World Construction
Caoliwen Wang, Mengdi Wang, Yige Chen +13
Constructing complex physical worlds from language requires coordinating extensive 3D environments, detailed structures and objects at different spatial scales, and interacting phy…
Eulerian-Lagrangian Fluid Simulation on Particle Flow Maps
Junwei Zhou, Duowen Chen, Molin Deng +5
We propose a novel Particle Flow Map (PFM) method to enable accurate long-range advection for incompressible fluid simulation. The foundation of our method is the observation that…
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…
Cellular Topology Optimization on Differentiable Voronoi Diagrams
Fan Feng, Shiying Xiong, Ziyue Liu +6
Cellular structures manifest their outstanding mechanical properties in many biological systems. One key challenge for designing and optimizing these geometrically complicated stru…
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…
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…
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…