Learning noisy phase transition dynamics from stochastic partial differential equations
The non-equilibrium dynamics of mesoscale phase transitions are fundamentally shaped by thermal fluctuations, which not only seed instabilities but actively control kinetic pathways, including rare barrier-crossing events such as nucleation that are entirely inaccessible to deterministic models. Machine-learning surrogates for such systems must therefore represent stochasticity explicitly, enforce conservation laws by construction, and expose physically interpretable structure. We develop physics-aware surrogate models for the stochastic Cahn-Hilliard equation in 3D that satisfy all three requirements simultaneously. The key innovation is to parameterize the surrogate at the level of inte...
| Authors | Luning Sun, Van Hai Nguyen, Shusen Liu et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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