Phase Redistribution and Separation Generation: A Component-Transport Framework for Controllable Three-Dimensional Microstructure Synthesis

Three-dimensional generative models have demonstrated substantial capabilities in synthesizing complex geometry, including point clouds, implicit surfaces, radiance fields, and volumetric representations. However, generating heterogeneous structures in which distinct material components form spatially organized phases presents a different challenge: the desired geometry may emerge from the redistribution, segregation, coarsening, and interfacial organization of multiple components rather than from the direct generation of a single geometric object. This paper proposes Phase Redistribution and Separation Generation (PRSG), a framework for controllable three-dimensional synthesis based on component-wise spatial fields and phase-evolution dynamics. The proposed method represents each constituent through a concentration field and couples component transport with interfacial regularization, compositional constraints, and morphology-conditioned control. A neural transition operator learns or approximates phase-evolution steps, while a morphology controller regulates properties such as phase volume fraction, characteristic domain size, interface area, and phase connectivity. The resulting fields can be converted into voxel volumes, meshes, implicit surfaces, or material-aware renderable representations. Unlike a purely static generator, PRSG is designed to expose the intermediate states of structural formation and permit direct control over the mechanisms that shape the final microstructure. We formulate the optimization objectives, describe a reproducible experimental protocol, and propose comparisons with physics-based phase-field simulation and existing generative baselines. The framework is intended for synthetic polymer-blend morphologies, immiscible-fluid configurations, multiphase porous materials, and related heterogeneous structures. Its effectiveness, computational efficiency, and advantages over existing methods remain empirical questions to be investigated.