使用符号回归模型建模生物结构的光学特性
Julian Sierra-Velez1, Alexandre Vial1, Demetrio Macías1
1University of Technology of Troyes, Laboratory Light, Nanomaterials and Nanotechnologies - L2n, and CNRS UMR 7076, 12 Rue Marie Curie, 10004 Troyes, France.
Physical review. E
|October 21, 2025
概括
本研究介绍了一种机器学习方法,使用符号回归来从光谱数据中找到生物材料光学性质的数学公式. 该方法产生了物理上有意义和可读的折射率模型,有助于材料的表征.
科学领域:
- 生物物理学的生物物理.
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 对生物材料的光学性能进行准确的建模对于各种应用至关重要.
- 现有的模型可能缺乏物理解释性,或与多样化的光谱数据作斗争.
- 开发用于导出光学属性的闭式表达式的方法是一个持续的挑战.
研究的目的:
- 介绍一种基于符号回归的机器学习方法,用于导出生物材料光学性质的闭式表达式.
- 用光谱数据 (数值和实验) 评估这种方法的性能.
- 在生物结构中检索构成材料的折射率.
主要方法:
- 利用符号回归,一种机器学习技术,来分析光谱数据.
- 将该方法应用于数值生成和实验测量的光谱数据.
- 进行了三个案例研究,重点是检索折射率.
主要成果:
- 成功推导出可读且尺寸均的分散模型.
- 得到的表达式具有物理意义.
- 衍生模型的代数形式与可见光谱中透明介电物的既定模型保持一致.
结论:
- 符号回归方法有效地模拟生物材料的光学特性.
- 衍生模型在物理上是可解释的,并且与传统方法相比较.
- 这种技术为表征生物材料的分散行为提供了一个强大的工具.
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