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使用方程告知神经网络的SAC305接构成方程的系数提取
Cadmus Yuan1, Qinghua Su2, Kuo-Ning Chiang2,3
1Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung 40724, Taiwan.
Materials (Basel, Switzerland)
|July 29, 2023
概括
方程告知神经网络 (EINNs) 有效地提取构成方程系数. 数字贝叶斯推理 (BI) 精确了这些系数,为像SAC305接材料这样的材料提供了值和分布.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
- 机器学习 机器学习
背景情况:
- 构成模型对于材料行为预测至关重要.
- 提取准确的材料参数可能是具有挑战性的.
- 现有的方法可能缺乏效率或全面的参数洞察力.
研究的目的:
- 开发一种有效的方法来提取构成方程系数.
- 通过数字贝叶斯推理来完善提取的系数.
- 将该方法应用于无SAC305接材料.
主要方法:
- 方程信息的神经网络 (EINNs) 用于系数提取.
- 数字贝叶斯推理 (BI) 用于系数分布估计.
- 在Garofalo,Anand和Chaboche模型中实现SAC305接.
主要成果:
- EINNs成功地提取了温度和拉伸率依赖模型的系数.
- 来自EINNs的系数的性能与现有研究可比.
- 该方法提供了系数的值和分布.
结论:
- EINNs提供了一种有效的方法来识别构成模型的参数.
- 结合EINNs和BI方法提高了系数的准确性,并提供了不确定性量化.
- 这种方法对于表征无材料,如SAC305.5,是有效的.
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