# Learning deterministic hydrodynamic equations from stochastic active particle dynamics

arXiv: https://arxiv.org/abs/2201.08623 · HTML: https://mlsanity.com/paper/2201.08623/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [DeepMoD: Deep learning for Model Discovery in noisy data](https://mlsanity.com/paper/1904.09406/) (2019)
- [OpenFPM: A scalable open framework for particle and particle-mesh codes on parallel computers](https://mlsanity.com/paper/1804.07598/) (2018)

## Built on by (most cited first, 2 of 2)

- [Learning stochastic dynamics and predicting emergent behavior using transformers](https://mlsanity.com/paper/2202.08708/) (2022)
- [Learning locally dominant force balances in active particle systems](https://mlsanity.com/paper/2307.14970/) (2023)

## Abstract (arXiv)

We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories. We apply our method to learning a hydrodynamic model for the propagating density lanes observed in self-propelled particle systems and to learning a continuum description of cell dynamics in epithelial tissues. We also infer from stochastic particle trajectories the latent phoretic fields driving chemotaxis. This demonstrates that statistical learning theory combined with physical priors can enable discovery of multi-scale models of non-equilibrium stochastic processes characteristic of collective movement in living systems.