Extended pseudo-spectral physics-informed neural networks for phase-field models

• URL: http://arxiv.org/abs/2606.24660v1
• Date: Tue, 23 Jun 2026 14:51:58 GMT
• Title: Extended pseudo-spectral physics-informed neural networks for phase-field models
• Abstract summary: Phase-field models play a central role in the continuum description of phase separation.<n>ESPINN framework is developed for inverse identification of phase-field models from transient snapshot data.<n>Results establish ESPINN as a data-efficient and physically consistent approach for learning free-energy structure in continuum models of phase separation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phase-field models play a central role in the continuum description of phase separation, in which the bulk free-energy density and the interfacial thickness parameter determine pattern formation and microstructural evolution. In practice, these constitutive quantities are rarely known a priori and must be inferred from limited dynamical observations. In this work, an extended pseudo-spectral physics-informed neural network (ESPINN) framework is developed for the inverse identification of phase-field models from transient snapshot data. It enables the simultaneous recovery of both the bulk chemical potential and unknown gradient coefficients. Numerical experiments on the one-dimensional Cahn-Hilliard equation demonstrate accurate and statistically stable reconstruction in the noiseless regime, with substantial constitutive information recoverable from even a single snapshot pair. In the presence of noise, reconstruction accuracy degrades gracefully, and increasing the number of snapshots improves robustness by reducing variance across runs. These results establish ESPINN as a data-efficient and physically consistent approach for learning free-energy structure in continuum models of phase separation.

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