在古典密度函数理论中,用于前向和反向潜在密度映射的神经运算符.
Runtong Pan1, Xinyi Fang2, Kamyar Azizzadenesheli3
1Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA.
The Journal of chemical physics
|October 28, 2025
概括
神经运算符在密度函数理论中有效地模拟复杂的关系. 里叶神经运算器 (FNO) 和DeepONet变种显示出有希望的结果,FNO在预测硬棒流体的多余自由能量方面表现出色.
科学领域:
- 计算物理 计算物理
- 统计力学 统计力学
- 机器学习 机器学习
背景情况:
- 神经运算符提供了一个数据驱动的方法来建模复杂的功能关系.
- 经典密度函数理论 (DFT) 涉及到物理量之间的复杂映射.
研究的目的:
- 评估神经操作员架构,以学习一维硬棒流体中的关系.
- 与密集的神经网络对比深度运营者网络 (DeepONet) 和富里埃神经运营者 (FNO).
主要方法:
- 训练有素的DeepONet和FNO变体对来自硬棒流体分析溶液的数据进行训练.
- 通过交叉验证评估了插值和外推能力.
- 比较平均二次误差和多余的自由能量预测准确度.
主要成果:
- 在预测多余的自由能量方面,FNO表现出卓越的准确性,特别是在平方 ReLU 激活的情况下.
- 在DeepONet变体中,GK-RMSCNN-DeepONet表现最好.
- 神经操作员映射 ρ c1 在解决密度配置文件方面被证明是有效的,其表现优于 Vext ρ 直接映射用于推断.
结论:
- 神经运算符,特别是FNO,对于模拟DFT中的功能关系非常有效.
- ρ c1映射为密度概况预测提供了一个强大的方法,在推断中具有优势.
- 专门的神经操作员功能提高了预测的准确性和灵活性.
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