Physics-informed operator learning for transferable energy-dissipative microstructure dynamics
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911661636452352 |
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| author | Xiong, Jie Wu, Yue Zhou, Xuewei Zhao, Peishuo Zhu, Jiaming |
| author_facet | Xiong, Jie Wu, Yue Zhou, Xuewei Zhao, Peishuo Zhu, Jiaming |
| contents | Phase-field simulations provide mechanistic descriptions of microstructure evolution, but repeated high-fidelity integration over long horizons and broad parameter spaces remains computationally expensive. We present PFNet, a physics-informed neural operator framework that advances microstructural states by learning conditional evolution operators rather than direct correlations. PFNet combines a diffusion-inspired U-Net with periodic padding, entropy-based state conditioning and thermodynamic-parameter modulation to encode boundary consistency, instantaneous ordering state and changes in the free-energy landscape. For Cahn-Hilliard coarsening, PFNet achieves accurate one-step prediction and stable autoregressive rollouts across composition, gradient-energy coefficient, coarsening stage and morphology class, with errors concentrated near diffuse interfaces and topology-changing regions. The same framework extends to a four-channel martensitic-transformation benchmark without martensite-specific redesign. These results indicate that physics-informed operator learning can provide transferable surrogates for phase-field dynamics and broader energy-dissipative dynamical systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_07279 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-informed operator learning for transferable energy-dissipative microstructure dynamics Xiong, Jie Wu, Yue Zhou, Xuewei Zhao, Peishuo Zhu, Jiaming Materials Science Disordered Systems and Neural Networks Phase-field simulations provide mechanistic descriptions of microstructure evolution, but repeated high-fidelity integration over long horizons and broad parameter spaces remains computationally expensive. We present PFNet, a physics-informed neural operator framework that advances microstructural states by learning conditional evolution operators rather than direct correlations. PFNet combines a diffusion-inspired U-Net with periodic padding, entropy-based state conditioning and thermodynamic-parameter modulation to encode boundary consistency, instantaneous ordering state and changes in the free-energy landscape. For Cahn-Hilliard coarsening, PFNet achieves accurate one-step prediction and stable autoregressive rollouts across composition, gradient-energy coefficient, coarsening stage and morphology class, with errors concentrated near diffuse interfaces and topology-changing regions. The same framework extends to a four-channel martensitic-transformation benchmark without martensite-specific redesign. These results indicate that physics-informed operator learning can provide transferable surrogates for phase-field dynamics and broader energy-dissipative dynamical systems. |
| title | Physics-informed operator learning for transferable energy-dissipative microstructure dynamics |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2605.07279 |