evoxels将三维显微数据与物理仿真结合，实现可微分的微观结构模拟。
evoxels: A differentiable physics framework for voxel-based microstructure simulations
- 基于体素的统一框架，用Python实现全链路可微分建模
- 支持从高分辨率显微图像到性能预测的端到端仿真
- 适合材料逆向设计与多尺度优化的研究者
材料科学跨学科特性显著：实验学家借助先进显微技术揭示微纳米尺度结构，理论与计算科学家则构建连接加工、结构与性能的模型。弥合这些领域对逆向材料设计至关重要——即从期望性能出发，反推最优微观结构与制造路径。融合高分辨率成像、预测性仿真与数据驱动优化，可加速发现并深化对工艺-结构-性能关系的理解。evoxels是一种可微分物理框架，采用全Python化的统一体素方法，集成分割后的3D显微数据、物理仿真、逆向建模与机器学习，实现从结构表征到性能预测的全流程可微分计算。
原文摘要 · Abstract (English)
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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