Speaker
Description
Phase-field models provide a fundamental continuum framework for describing phase separation and pattern formation in many physical and biological systems. Their predictive capability depends critically on constitutive quantities such as the bulk free-energy density and interfacial thickness parameter, which are often unknown and must be inferred from limited observations. In this work, we introduce an extended pseudo-spectral physics-informed neural network (ESPINN) framework for the inverse identification of phase-field models from transient snapshot data. The proposed method simultaneously reconstructs the bulk chemical potential and unknown gradient coefficients directly from dynamically evolving structures.
Numerical experiments show that ESPINN accurately recovers both the functional form of the free energy and the interfacial thickness parameter. Remarkably, substantial constitutive information can be extracted even from a single snapshot pair, while additional snapshots improve robustness and reduce variance across training runs. The framework remains stable in the presence of noise, with reconstruction accuracy improving as more observations are incorporated. These results highlight ESPINN as a data-efficient and physically consistent approach for learning constitutive structure in continuum models of phase separation.
