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自信地预测压力-应变曲线:通过双贝叶斯模型的数据最小化和不确定性量化之间的平衡
Tianyi Li1,2, Zhengyuan Chen1,2, Zhen Zhang3
1SOlids inFormaTics AI-Laboratory (SOFT-AI-Lab), Sichuan University, Chengdu 610065, China.
Polymers
|February 26, 2025
概括
这项研究引入了一种双贝叶斯模型,用于预测聚合物应力-应变曲线,有效地管理有限的数据,并为可靠的材料属性预测量定量定量定量定量和认识不确定性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 聚合物科学 聚合物科学
背景情况:
- 机器学习 (ML) 显示出预测聚合物应力-张力曲线的前景.
- 挑战包括数据稀缺性,固有的处理属性可变性 (选择性不确定性) 和模型信心 (系统性不确定性).
研究的目的:
- 开发一个不确定性意识的框架,用于准确的应力-应变曲线预测.
- 在聚合物加工中区分aleatoric和epistemic的不确定性.
- 为了尽量减少数据要求,同时确保预测可靠性.
主要方法:
- 利用一个双贝叶斯模型整合贝叶斯神经网络 (BNNs).
- 使用Taguchi数组进行高效的数据采样 (4D空间中的27个样本).
- 嵌入隐藏和输出分布层用于不确定性量化.
主要成果:
- 双贝叶斯模型准确地预测了应力-应变曲线.
- 成功地区分了 aleatoric 和 epistemic 的不确定性.
- 为预测带有适度不确定性的预测提供了95%的置信区间.
结论:
- 开发的框架平衡了数据最小化和强大的不确定性量化.
- 即使使用有限的数据,也可实现可靠的材料性能预测.
- 为聚合物科学中的不确定性意识机器学习提供了一种新的方法.
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