13 citations · 31 across the 10 of their papers we have counts for
9 papers · 1 filter
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…