Physics-informed neural operator for predictive parametric phase-field modelling
This work addresses the problem of high-throughput parametric studies in materials science by providing a more efficient computational tool, though it is incremental as it builds on existing neural operator frameworks.
The authors tackled the computational intensity of phase-field modeling for materials evolution by developing PF-PINO, a physics-informed neural operator that embeds physical constraints into training, resulting in significant improvements in accuracy, generalization, and long-term stability over conventional methods like FNO.
Predicting the microstructural and morphological evolution of materials through phase-field modelling is computationally intensive, particularly for high-throughput parametric studies. While neural operators such as the Fourier neural operator (FNO) show promise in accelerating the solution of parametric partial differential equations (PDEs), the lack of explicit physical constraints, may limit generalisation and long-term accuracy for complex phase-field dynamics. Here, we develop a physics-informed neural operator framework to learn parametric phase-field PDEs, namely PF-PINO. By embedding the residuals of phase-field governing equations into the data-fidelity loss function, our framework effectively enforces physical constraints during training. We validate PF-PINO against benchmark phase-field problems, including electrochemical corrosion, dendritic crystal solidification, and spinodal decomposition. Our results demonstrate that PF-PINO significantly outperforms conventional FNO in accuracy, generalisation capability, and long-term stability. This work provides a robust and efficient computational tool for phase-field modelling and highlights the potential of physics-informed neural operators to advance scientific machine learning for complex interfacial evolution problems.