# evoxels: A differentiable physics framework for voxel-based microstructure simulations

arXiv: https://arxiv.org/abs/2507.21748 · HTML: https://mlsanity.com/paper/2507.21748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [On Neural Differential Equations](https://mlsanity.com/paper/2202.02435/) (2022)
- [Generating 3D structures from a 2D slice with GAN-based dimensionality expansion](https://mlsanity.com/paper/2102.07708/) (2021)
- [torchode: A Parallel ODE Solver for PyTorch](https://mlsanity.com/paper/2210.12375/) (2022)
- [A simple GPU implementation of spectral-element methods for solving 3D Poisson type equations on rectangular domains and its applications](https://mlsanity.com/paper/2310.00226/) (2023)

## Abstract (arXiv)

Materials science inherently spans disciplines: experimentalists use advanced microscopy to uncover micro- and nanoscale structure, while theorists and computational scientists develop models that link processing, structure, and properties. Bridging these domains is essential for inverse material design where you start from desired performance and work backwards to optimal microstructures and manufacturing routes. Integrating high-resolution imaging with predictive simulations and data-driven optimization accelerates discovery and deepens understanding of process-structure-property relationships. The differentiable physics framework evoxels is based on a fully Pythonic, unified voxel-based approach that integrates segmented 3D microscopy data, physical simulations, inverse modeling, and machine learning.