# Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss

arXiv: https://arxiv.org/abs/2506.15902 · HTML: https://mlsanity.com/paper/2506.15902/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Walls](https://mlsanity.com/paper/2506.15958/) (2025)
- [Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss](https://mlsanity.com/paper/2510.15664/) (2025)
- [Controlling Topological Defects in Polar Fluids via Reinforcement Learning](https://mlsanity.com/paper/2507.19298/) (2025)
- [Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems](https://mlsanity.com/paper/2607.16177/) (2026)

## Abstract (arXiv)

Optimal path planning and control of microscopic devices navigating in fluid environments is essential for applications ranging from targeted drug delivery to environmental monitoring. These tasks are challenging due to the complexity of microdevice-flow interactions. We introduce a closed-loop control method that optimizes a discrete loss (ODIL) in terms of dynamics and path objectives. In comparison with reinforcement learning, ODIL is more robust, up to three orders faster, and excels in high-dimensional action/state spaces, making it a powerful tool for navigating complex flow environments.