5 papers
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:…
Trajectory Consistency for One-Step Generation on Euler Mean Flows
Zhiqi Li, Yuchen Sun, Duowen Chen +2
We propose \emph{Euler Mean Flows (EMF)}, a flow-based generative framework for one-step and few-step generation that enforces long-range trajectory consistency with minimal sampli…
A Level Set Method on Particle Flow Maps
Jinjin He, Taiyuan Zhang, Zhiqi Li +3
This paper introduces a Particle Flow Map Level Set (PFM-LS) method for high-fidelity interface tracking. We store level-set values, gradients, and Hessians on particles concentrat…
An Adjoint Method for Differentiable Fluid Simulation on Flow Maps
Zhiqi Li, Jinjin He, Barnabás Börcsök +6
This paper presents a novel adjoint solver for differentiable fluid simulation based on bidirectional flow maps. Our key observation is that the forward fluid solver and its corres…