一种通用深度学习方法,通过解决反向问题来计算粘弹性构成模型的分子参数
Minghui Ye1, Yuan-Qi Fan1, Xue-Feng Yuan1
1Institute for Systems Rheology, Guangzhou University, No. 230 West Outer Ring Road, Higher Education Mega-Center, Panyu District, Guangzhou 510006, China.
Polymers
|September 9, 2023
概括
一种新的深度神经网络 (DNN) 方法可以从流体粘弹性特性准确预测分子参数. 这种方法为研究和工业中复杂的流体建模和配方设计提供了强大的解决方案.
科学领域:
- 类风病学 类风病学 类风病学
- 聚合物物理 聚合物物理
- 计算流体动力学的流体动力学.
背景情况:
- 从宏观粘弹性特性预测分子参数对于设计复杂流体至关重要.
- 当前的方法在各种应用中面临着准确性和稳健性的挑战.
研究的目的:
- 通过解决反向问题,开发一种通用的深度学习方法来计算粘弹性模型的分子参数.
- 为了验证基于深度神经网络 (DNN) 的解决方案的准确性,收性和稳定性.
主要方法:
- 深度神经网络 (DNN) 被用作逆向问题的数值解答器.
- 罗利-波利模型被用来模拟纠聚合物溶液的线性和非线性类型的类型.
- 在压力数据中,DNN解决器的性能与各种度和噪声水平进行了测试.
主要成果:
- 基于DNN的解决器表现出快速的融合和对微小的输入数据噪声的稳定性.
- 对单分散线性羊羔DNA溶液的分子参数的准确预测在广泛的剪切速率和度中得到了实现.
- 该方法成功预测了权力定律的度缩放,与实验估计密切匹配.
结论:
- 深度神经网络提供了一种有效和高效的工具,用于解决学中的反向问题.
- 经过验证的DNN溶解器准确计算了分子参数,为复杂的流体推进了分子和配方设计.
- 这种方法提高了基于粘弹性质的材料功能的预测能力.
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