将物理单位集成到高性能人工智能驱动的科学计算中
Chaoming Wang1, Sichao He2, Shouwei Luo3,4
1School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871, China.
Nature communications
|April 16, 2025
概括
SAIUnit将物理单元集成到人工智能 (AI) 科学计算库中. 该系统确保了人工智能研究的单元一致性,提高了准确性和可靠性,而不会牺牲性能.
科学领域:
- 科学计算科学计算
- 人工智能的人工智能
- 基于物理的机器学习
背景情况:
- 科学研究依赖物理单位进行准确的计算.
- 目前的人工智能图书馆缺乏对物理单元的原生支持,阻碍了科学整合.
- 这种差距阻碍了可靠的AI驱动科学工具的开发.
研究的目的:
- 介绍SAIUnit,这是一个用于将物理单元集成到AI科学计算中的新系统.
- 确保兼容JAX,一个流行的AI框架.
- 提高AI在科学研究中的准确性,可靠性和可解释性.
主要方法:
- 开发了SAIUnit,拥有2000多个物理单元和500个单元意识功能.
- 确保与JAX转换完全兼容 (自动区分,JIT编译等). ) 的情况.
- 测试了SAIUnit在各种AI驱动的科学领域,如数值集成和大脑建模.
主要成果:
- 在JAX转换过程中,SAIUnit保持了单元一致性.
- 在编译过程中检查单元可以提高准确性和可靠性.
- 在数值集成,大脑建模和基于物理的神经网络方面表现出有效性.
- 在运行时性能方面没有发现任何妥协.
结论:
- SAIUnit弥合了抽象的人工智能框架和物理单位之间的差距.
- 它使人工智能驱动的科学计算更强大,更有物理依据.
- 该系统增强了科学计算的可解释性和协作潜力.
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