English(EN) Thermally Conditioned Time-Series Forecasting of Grain Growth in Polycrystalline Materials

人工智能精确预测复杂热条件下的材料晶粒生长

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了一个新的框架，用于预测非等温条件下多晶材料中晶粒的生长。该方法结合了卷积神经网络来编码晶粒尺寸分布，多层感知机来处理热描述符，以及循环网络来进行时间预测。该模型在前端跟踪模拟数据上进行训练，能够准确重现各种热历史下的晶粒生长动力学，在交叉验证中实现了较低的平均相对误差。 AI

影响该人工智能框架为预测材料性能提供了比传统模拟计算效率更高的方法，有望加速材料科学的研究和开发。

排序理由详细介绍用于科学模拟问题的创新人工智能框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Eliane Younes, Elie Hachem, Marc Bernacki · 2026-11-05 05:00

多晶材料晶粒生长热力学条件时间序列预测

arXiv:2611.06843v1 Announce Type: cross Abstract: Grain growth during heat treatment strongly affects the microstructure of polycrystalline materials and, consequently, their mechanical properties. Although numerical methods such as front-tracking or front-capturing simulations c…