# Fluidic Topology Optimization with an Anisotropic Mixture Model

arXiv: https://arxiv.org/abs/2209.10736 · HTML: https://mlsanity.com/paper/2209.10736/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation](https://mlsanity.com/paper/2311.12194/) (2023)
- [EmbodiedOcc++: Boosting Embodied 3D Occupancy Prediction with Plane Regularization and Uncertainty Sampler](https://mlsanity.com/paper/2504.09540/) (2025)
- [NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation](https://mlsanity.com/paper/2405.14903/) (2024)
- [DualMS: Implicit Dual-Channel Minimal Surface Optimization for Heat Exchanger Design](https://mlsanity.com/paper/2504.02830/) (2025)
- [Automatic Truss Design with Reinforcement Learning](https://mlsanity.com/paper/2306.15182/) (2023)

## Abstract (arXiv)

Fluidic devices are crucial components in many industrial applications involving fluid mechanics. Computational design of a high-performance fluidic system faces multifaceted challenges regarding its geometric representation and physical accuracy. We present a novel topology optimization method to design fluidic devices in a Stokes flow context. Our approach is featured by its capability in accommodating a broad spectrum of boundary conditions at the solid-fluid interface. Our key contribution is an anisotropic and differentiable constitutive model that unifies the representation of different phases and boundary conditions in a Stokes model, enabling a topology optimization method that can synthesize novel structures with accurate boundary conditions from a background grid discretization. We demonstrate the efficacy of our approach by conducting several fluidic system design tasks with over four million design parameters.