Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics
Researchers propose an attention-based, physics-guided convolutional neural network as a surrogate model to predict microstructural evolution in systems governed by the Cahn-Hilliard equation. The model accurately forecasts phase separation in binary mixtures over long times, preserves composition, and aligns with the Lifshitz-Slyozov domain-growth law.
[2606.26128] Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics
[Submitted on 9 Jun 2026]
Title:Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics
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Abstract:The spatiotemporal evolution of many physical, chemical, and biological systems is described by nonlinear partial differential equations (PDEs). Recently, deep neural network-based surrogate models have gained increasing interest as efficient alternatives to computationally expensive traditional numerical solvers. In this work, we propose an attention-based, physics-guided convolutional neural network as a surrogate model to learn the microstructural evolution of such systems. We train the model to accurately predict the full time-evolution of phase separation in binary mixtures governed by the Cahn-Hilliard equation. We show that predictions from our trained surrogate model remain stable and accurate over long-time rollouts for both critical and off-critical mixtures and preserve the mixture composition throughout evolution. We also show that our model accurately captures the growth of domain size and is consistent with the Lifshitz-Slyozov domain-growth law. The prediction results demonstrate the effectiveness of the proposed framework for modeling systems with conserved kinetics and can be extended to other complex dynamical systems.
Subjects:
Machine Learning (cs.LG); Soft Condensed Matter (cond-mat.soft)
Cite as: arXiv:2606.26128 [cs.LG]
(or arXiv:2606.26128v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.26128
arXiv-issued DOI via DataCite
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From: Vijay Yadav [view email] [v1] Tue, 9 Jun 2026 10:16:50 UTC (4,374 KB)
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