来自实验的软材料的构成关系的无偏构建,通过神经系统网络通过神经系统信息传递
Mohammadamin Mahmoudabadbozchelou1, Krutarth M Kamani2, Simon A Rogers2
1Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115.
概括
这项研究使用科学机器学习来发现复杂流体的特定材料构成方程. 这种方法准确地模拟了软材料的行为,并从有限的实验数据中揭示了潜在的物理学.
科学领域:
- 软物质物理学 软物质物理学
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 软材料的传统构成关系依赖于简化假设,限制特定材料的准确性和物理洞察力.
- 现有的模型难以捕捉流体中复杂的,时间和速度依赖的应力反应.
- 传统框架中的偏见往往阻碍了对新物理现象的发现.
研究的目的:
- 开发一种以数据为导向的方法,用于发现复杂流体的精确,特定材料的构成方程.
- 克服软物质科学中传统构成模型的局限性.
- 为了利用科学机器学习来揭示隐藏的风湿行为.
主要方法:
- 采用了基于质的神经网络框架.
- 利用复杂流体的实验数据来训练机器学习模型.
- 专注于发现封闭形式的构成关系.
主要成果:
- 神经网络框架从有限的实验中成功地学会了复杂流体的隐藏的形学.
- 建立了一个无偏见的,特定于材料的构成关系.
- 发现的模型准确地描述了材料的广泛动态行为.
结论:
- 科学机器学习为发现复杂软材料的构成方程提供了一种有效的方法.
- 这种数据驱动的方法提供了准确的,材料特定的模型,没有固有的偏见.
- 该框架不仅模拟了行为,还提供了对控制材料的基本物理学的见解.
相关概念视频
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