PFNet learns physics-informed operators to deliver accurate one-step and stable multi-step predictions of microstructure dynamics in Cahn-Hilliard coarsening and martensitic transformations across varying conditions.
Small positional shifts of diffuse interfaces therefore produce disproportionately large pointwise residuals
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Physics-informed operator learning for transferable energy-dissipative microstructure dynamics
PFNet learns physics-informed operators to deliver accurate one-step and stable multi-step predictions of microstructure dynamics in Cahn-Hilliard coarsening and martensitic transformations across varying conditions.