# Variational Inference via Smoothed Particle Hydrodynamics

arXiv: https://arxiv.org/abs/2407.09186 · HTML: https://mlsanity.com/paper/2407.09186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 82 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm](https://mlsanity.com/paper/1608.04471/) (2016)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Particle-based Energetic Variational Inference](https://mlsanity.com/paper/2004.06443/) (2020)
- [Classification via score-based generative modelling](https://mlsanity.com/paper/2207.11091/) (2022)
- [Electrostatics-based particle sampling and approximate inference](https://mlsanity.com/paper/2406.20044/) (2024)
- [Dynamic Simulation of Splashing Fluids](https://mlsanity.com/paper/2302.06087/) (2023)

## Built on by (most cited first, 1 of 1)

- [Variational Inference Using Material Point Method](https://mlsanity.com/paper/2407.20287/) (2024)

## Abstract (arXiv)

A new variational inference method, SPH-ParVI, based on smoothed particle hydrodynamics (SPH), is proposed for sampling partially known densities (e.g. up to a constant) or sampling using gradients. SPH-ParVI simulates the flow of a fluid under external effects driven by the target density; transient or steady state of the fluid approximates the target density. The continuum fluid is modelled as an interacting particle system (IPS) via SPH, where each particle carries smoothed properties, interacts and evolves as per the Navier-Stokes equations. This mesh-free, Lagrangian simulation method offers fast, flexible, scalable and deterministic sampling and inference for a class of probabilistic models such as those encountered in Bayesian inference and generative modelling.