PINN-Phase: A physics-informed neural network for curvature-driven multiphase-field evolution
TL;DR
PINN-Phase is introduced, a physics-informed neural time integrator that advances the full multiphase field from its initial condition and enforces phase bounds and unit sum at every step; on the reported explicit multiphase-field benchmarks, post-initial-condition reference states serve only for evaluation.
Abstract
Phase-field simulation of polycrystalline microstructures becomes costly when many related cases must be evolved over long times. We introduce PINN-Phase, a physics-informed neural time integrator that advances the full multiphase field from its initial condition and enforces phase bounds and unit sum at every step. On the reported explicit multiphase-field path, it learns from the governing residual on its own predicted states, without post-initial-condition reference states in the loss or checkpoint selection. A fixed 25-grain model satisfies the complete predefined criteria in seven of eight prospectively fixed unseen microstructures and in both deliberately atypical stress cases, with 0.94-3.71% terminal label disagreement. At native 128^3 resolution, an eight-grain model trained on four initial microstructures retains 98.79% label agreement and exact survivor identities on a training case after 24,000 autonomous steps, 5.86 times its represented training horizon. On six prospectively fixed unseen 128^3 initial conditions, label disagreement at the last saved state within that horizon is 0.72-2.11%; after autonomous evolution to 24,000 steps, or 5.86 times the represented training horizon, five of six retain the exact terminal survivor set. At 64 grains, terminal disagreement decreases from 6.09% in the primary evaluation to 3.97% after continued training, with exact recovery of the 21-grain survivor set. These results demonstrate structurally admissible long-horizon neural integration and direct initial-condition transfer within fixed benchmark families.