Uncertainty-Aware Crack Growth Forecasting via Conditional Denoising Diffusion Models for Phase-Field Fracture

The accurate prediction of brittle crack initiation, propagation, and complex topological evolution remains computationally prohibitive when utilizing traditional high-fidelity phase-field finite element methods. To overcome these computational bottlenecks, a physics-informed conditional Denoising Diffusion Probabilistic Model (DDPM) is proposed for the full-field spatiotemporal forecasting of fracture evolution across diverse loading regimes and energy decomposition methods. The generative architecture is conditioned on rolling historical damage states and explicitly derived kinematic proxies -- phase-field velocity and gradient magnitude -- ensuring temporal coherence without non-physical artifacts.
The principal contribution is spatially-localized uncertainty quantification without modification to the trained model. Ensemble variance concentrates at crack branching junctions ($\sigma_\mathrm{max} = 0.222$ at Y-junction bifurcations; zero high-uncertainty pixels in four deterministic propagation cases), while the high-$\sigma$ tail identifies high-error predictions with 90\% precision -- an 18-fold improvement over random selection. One-step crack tip localization achieves sub-pixel accuracy (0.12 px mean error) across both held-out validation subsets (shear-star and tension-spect), confirming cross-regime generalization. In closed-loop autoregressive rollout over 50 steps, the DDPM maintains Dice = 0.929 $\pm$ 0.010 while a deterministic U-Net collapses to Dice = 0.423 under error accumulation, a 2.2$\times$ gap that establishes the value of stochastic re-sampling for long-horizon stability. Per-step inference requires approximately 3.6 s on an H100 GPU, approximately 28$\times$ faster than the FEM reference and 1{,}000$\times$ slower than a deterministic U-Net a cost that buys the stochastic diversity enabling uncertainty quantification.