海洋网:一个以原则为基础的基于神经运营者的数字双胞胎,用于区域海洋
Ashesh Chattopadhyay1, Michael Gray2, Tianning Wu2
1Applied Mathematics, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA. aschatto@ucsc.edu.
Scientific reports
|September 11, 2024
概括
新的人工智能模型OceanNet准确地预测了海面的海流高度,比如海湾流. 这个数字双胞胎为传统海洋模型提供了50万倍更快,更具成本效益的替代方案.
科学领域:
- 海洋学 海洋学 海洋学
- 人工智能的人工智能
- 计算流体动力学的流体动力学.
背景情况:
- 海洋建模面临着复杂的水度,陆地相互作用,垂直结构和流动非线性等挑战.
- 数据驱动的方法有希望,但需要适应海洋的复杂性.
- 现有的数值海洋模型是计算密集的.
研究的目的:
- 介绍OceanNet,一个基于神经操作员的数字双胞胎,用于区域海面高度仿真.
- 用物理启发的深度学习方法解决海洋建模方面的挑战.
- 为了使成本有效和高效的季节性预测海洋流.
主要方法:
- 利用富里埃神经运算符架构来模拟海面高度.
- 实施了预测器-评估器-校正器集成方案,以提高稳定性和减轻错误增长.
- 整合了一种光谱调节器,以抵消在较小尺度上的光谱偏差.
- 训练了OceanNet对西北大西洋的历史海面高度数据进行训练.
主要成果:
- 与最先进的动态海洋模型相比,OceanNet取得了竞争力的预测技能.
- 证明了计算成本的显著降低,降低了50万倍.
- 成功模拟了海流和环流的海面高度.
结论:
- 灵感来自物理学的深度神经运算符为高分辨率数值海洋模型提供了可行和高效的替代方案.
- 海洋网在数据驱动的海洋建模和预测方面取得了重大进展.
- 这种方法显示出在气候和海洋学研究中具有更广泛应用的潜力.
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