用机器学习预测面心立方合金的塑性变形与应力应变，还能给出不确定性分析。
Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
- 用混合密度网络预测位错密度和局部应力分布的分布特征。
- 在晶粒尺度上实现应力-应变关系的高精度预测，并量化误差范围。
- 适合材料设计与性能优化，尤其关注可靠性要求高的场景。
机器学习显著推动了结构材料的理解与应用，日益重视数据整合与预测模型中的不确定性量化。本研究提出一种综合方法，采用混合密度网络（MDN）模型，基于大量文献实验数据训练。该方法独特地预测位错密度（作为潜在变量）及晶粒尺度上的应力分布概率分布。将这些预测分布的统计参数引入位错介导的塑性模型，实现了具有明确不确定性量化能力的应力-应变精准预测。该策略不仅提升了机械性能预测的准确性和可靠性，还在合金设计优化中发挥关键作用，助力快速演进产业中新材料的开发。
原文摘要 · Abstract (English)
Machine learning has significantly advanced the understanding and application of structural materials, with an increasing emphasis on integrating existing data and quantifying uncertainties in predictive modeling. This study presents a comprehensive methodology utilizing a mixed density network (MDN) model, trained on extensive experimental data from literature. This approach uniquely predicts the distribution of dislocation density, inferred as a latent variable, and the resulting stress distribution at the grain level. The incorporation of statistical parameters of those predicted distributions into a dislocation-mediated plasticity model allows for accurate stress-strain predictions with explicit uncertainty quantification. This strategy not only improves the accuracy and reliability of mechanical property predictions but also plays a vital role in optimizing alloy design, thereby facilitating the development of new materials in a rapidly evolving industry.
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