通过数据驱动的战略,对同位素辅助性元材料进行反向设计
Ertai Cao1, Zhicheng Dong2, Ben Jia2
1School of Aeronautics, Northwestern Polytechnical University, Xi'an, 710072, China. huangheyuan@nwpu.edu.cn.
Materials horizons
|April 22, 2025
概括
本研究介绍了使用科尔莫戈罗夫-阿诺德网络 (KANs) 进行精确辅助性元材料设计的数据驱动设计策略. 这种方法显著提高了准确性和计算效率,用于创建同otropic 机械反应.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 由于几何不确定性,设计同源性辅助性元材料具有挑战性.
- 精确预测机械性能和反向设计对于元材料的开发至关重要.
研究的目的:
- 引入一种创新的数据驱动的结构设计策略,用于同源辅助性超材料.
- 为了能够准确地预测机械性能和反向设计.
- 利用科尔摩戈罗夫-阿诺德网络 (KANs) 来精确地绘制设计参数和机械响应之间的映射.
主要方法:
- 使用可学习的激活函数的Kolmogorov-Arnold网络 (KAN).
- 开发了一个数据驱动的反向设计策略.
- 利用遗传算法优化超材料结构.
主要成果:
- 实现了压力预测的平均平方误差 (MSE) 低至0.81%,超过了多层感知子 (MLP) 模型.
- 已证明计算效率超过有限元方法的10^3倍.
- 优化的超材料结构 (模型III) 实现了0.05%的平均MSE,大大改善了原始 (模型I) 和扰乱 (模型II) 结构.
结论:
- 数据驱动的反向设计方法为精确的辅助性元材料设计提供了强大而高效的工具.
- 成功实现了通过实验和有限元素分析验证的同位素机械反应设计.
- 这种方法对灵活可穿戴设备和组织工程的应用非常有希望.
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