Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics

• URL: http://arxiv.org/abs/2606.26128v1
• Date: Tue, 09 Jun 2026 10:16:50 GMT
• Title: Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics
• Abstract summary: We propose an attention-based physics-guided convolutional neural network as a surrogate model to learn the evolution of such systems.<n>We show that predictions from our trained surrogate model remain stable and accurate over long-time rollouts for both critical and off-critical mixtures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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