Extended pseudo-spectral physics-informed neural networks for phase-field models
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 Ca...
| Authors | Callum Marsh, Radek Erban, Andreas Munch |
|---|---|
| Primary category | q-bio.QM |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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