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

• URL: http://arxiv.org/abs/2507.21748v1
• Date: Tue, 29 Jul 2025 12:29:15 GMT
• Title: evoxels: A differentiable physics framework for voxel-based microstructure simulations
• Authors: Simon Daubner, Alexander E. Cohen, Benjamin Dörich, Samuel J. Cooper,
• Abstract summary: 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.
• Score: 41.94295877935867
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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