Physics-informed neural operator for parametric phase-field modelling of interfacial degradation and microstructural evolution
Predicting interfacial degradation and microstructural evolution through phase-field modelling is computationally intensive, particularly for parametric studies. While neural operators such as the Fourier neural operator (FNO) offer a promising route to acceleration, the absence of physical constraints compromises their generalisation and long-term stability. Here, we present PF-PINO, a physics-informed neural operator framework that embeds phase-field governing equation residuals directly into the training loss, together with gradient normalisation loss balancing and a staggered training scheme for coupled multi-field systems. PF-PINO is validated on four benchmarks including pencil-elec...
| Authors | Nanxi Chen, Airong Chen, Rujin Ma |
|---|---|
| Primary category | cs.LG |
| Published | 10 March 2026 |
| Last updated at source | 3 October 2026 |
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