13 citations · 31 across the 10 of their papers we have counts for
13 papers
From Cauchy to Gaussian: Sharp Lower Deviations and Ehrhard-Type Inequality
Yong Huang, Shengyu Tang, Sinan Wang
We establish dimension-free, pointwise optimal lower-deviation bounds for convex functions under spherically symmetric Cauchy-type measures. These bounds use either mean-variance n…
LoCoSplat: Real-Time Feed-Forward 3D Gaussian Splatting with Minimal 3D Reasoning
Sinan Wang, Jinjin He, Yuchen Sun +3
Feed-forward 3D Gaussian Splatting (3DGS) increasingly aggregates multi-view evidence with heavy learned 3D networks. We propose LoCoSplat (Local-Context Splatting), motivated by t…
Hamiltonian Two-Way Coupling of Nonlinear Waves and 3D Flows
Sinan Wang, Ruicheng Wang, Taiyuan Zhang +5
Simulating large-scale free-surface water by coupling a localized 3D fluid solver to a cheaper 2D surface model has long faced a mismatch in wave dynamics: efficient 2D wave models…
On The Minkowski Problem With Respect To A Mixed Euclidean-Gaussian Density
Stephanie Mui, Lei Qin, Sinan Wang
In this paper, we pose a new class of Minkowski problems corresponding to the mixed Euclidean-Gaussian volume. Using the flow method, we prove the existence of smooth normalized so…
Brunn-Minkowski inequality for weighted dual quermassintegrals
Xiaojuan Chen, Shengyu Tang, Sinan Wang
We investigate the Brunn-Minkowski inequality for dual quermassintegrals in weighted measure spaces, which is a special class of rotationally invariant measures proposed by C…
Minkowski problem and Brunn-Minkowski inequality for dual quermassintegrals
Xiaojuan Chen, Shengyu Tang, Sinan Wang
This paper studies the core problems in the dual Brunn-Minkowski theory, encompassing the Minkowski problem and Brunn-Minkowski inequality for dual quermassintegr…
Hermite-NGP: Gradient-Augmented Hash Encoding for Learning PDEs
Jinjin He, Zhiqi Li, Sinan Wang +1
We propose Hermite-NGP, a gradient-augmented multi-resolution hash encoding designed to enable fast and accurate computation of spatial derivatives for neural PDE solvers. Unlike e…
Generative Modeling with Orbit-Space Particle Flow Matching
Sinan Wang, Jinjin He, Shenyifan Lu +3
We present Orbit-Space Geometric Probability Paths (OGPP), a particle-native flow-matching framework for generative modeling of particle systems. OGPP is motivated by two insights:…
An Impulse-formed Navier-Stokes Solver based on Long-range Particle Flow Maps
Zhiqi Li, Duowen Chen, Junwei Zhou +3
We present a particle-grid characteristic-mapping framework that extends long-range characteristic mapping from inviscid flows to general Navier-Stokes dynamics with viscosity, bod…
Fluid Simulation on Vortex Particle Flow Maps
Sinan Wang, Junwei Zhou, Fan Feng +5
We propose the Vortex Particle Flow Map (VPFM) method to simulate incompressible flow with complex vortical evolution in the presence of dynamic solid boundaries. The core insight…
An Eulerian Vortex Method on Flow Maps
Sinan Wang, Yitong Deng, Molin Deng +6
We present an Eulerian vortex method based on the theory of flow maps to simulate the complex vortical motions of incompressible fluids. Central to our method is the novel incorpor…
SENC: Handling Self-collision in Neural Cloth Simulation
Zhouyingcheng Liao, Sinan Wang, Taku Komura
We present SENC, a novel self-supervised neural cloth simulator that addresses the challenge of cloth self-collision. This problem has remained unresolved due to the gap in simulat…
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…