# Control of flow behavior in complex fluids using automatic differentiation

arXiv: https://arxiv.org/abs/2403.06257 · HTML: https://mlsanity.com/paper/2403.06257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2024.

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

- [Meshless solutions of PDE inverse problems on irregular geometries](https://mlsanity.com/paper/2510.25752/) (2025)
- [A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics](https://mlsanity.com/paper/2604.07129/) (2026)

## Abstract (arXiv)

Inverse design of complex flows is notoriously challenging because of the high cost of high dimensional optimization. Usually, optimization problems are either restricted to few control parameters, or adjoint-based approaches are used to convert the optimization problem into a boundary value problem. Here, we show that the recent advances in automatic differentiation (AD) provide a generic platform for solving inverse problems in complex fluids. To demonstrate the versatility of the approach, we solve an array of optimization problems related to active matter motion in Newtonian fluids, dispersion in structured porous media, and mixing in journal bearing. Each of these problems highlights the advantages of AD in ease of implementation and computational efficiency to solve high-dimensional optimization problems involving particle-laden flows.