融合物理规律的图网络与LSTM模型，高效预测材料微观结构长期演化。
Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution
- 基于图卷积与LSTM的联合架构，运行于潜在图空间实现高效建模。
- 在多组分、二维与三维场景下均实现长期演化预测，计算成本低。
- 适合材料科学领域需长期模拟微观结构演化的研究者使用。
本文提出一种融合物理规律的框架，将图卷积网络（GCN）与长短期记忆（LSTM）结合，用于在2D和3D条件下对微观结构演化进行长期预测，表现优异。该框架具备组分感知能力，可在不同组分数据集上联合训练，并在潜在图空间中运行，从而同时捕捉组分与形貌动态，保持计算高效。通过卷积自编码器压缩并编码相场仿真数据，使模型能在不同组分、维度及长期时间尺度下实现高效演化建模。框架能有效捕获微观结构的空间-时间演变模式，且在训练后可实现远距离预测，计算开销显著降低。
原文摘要 · Abstract (English)
This paper presents a physics-informed framework that integrates graph convolutional networks (GCN) with long short-term memory (LSTM) architecture to forecast microstructure evolution over long time horizons in both 2D and 3D with remarkable performance across varied metrics. The proposed framework is composition-aware, trained jointly on datasets with different compositions, and operates in latent graph space, which enables the model to capture compositions and morphological dynamics while remaining computationally efficient. Compressing and encoding phase-field simulation data with convolutional autoencoders and operating in Latent graph space facilitates efficient modeling of microstructural evolution across composition, dimensions, and long-term horizons. The framework captures the spatial and temporal patterns of evolving microstructures while enabling long-range forecasting at reduced computational cost after training.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。