用物理约束的机器学习模型，高效模拟混沌气候系统。
Modeling chaotic Lorenz ODE System using Scientific Machine Learning
- 融合物理规律与神经网络，构建数据高效模型。
- 在少量数据下实现高精度气候预测，提升可解释性。
- 适合气候建模、政策制定者及对可解释性要求高的场景。
在气候科学中，全球变暖和天气预测模型因高质量数据稀缺而面临挑战，数据效率至关重要。近年来，科学机器学习（SciML）因其数据高效训练能力受到广泛关注，特别适用于真实气候场景。然而，针对混沌气候系统的SciML研究仍寥寥无几。本文将SciML方法融入基础天气模型，通过物理信息驱动的方法，在减少数据依赖的同时实现高精度大规模气候预测。结果表明，结合物理模型的可解释性与神经网络的计算能力，SciML能成为可靠的气候建模工具，推动从传统黑箱机器学习向物理信息决策的转变，助力有效气候政策实施。
原文摘要 · Abstract (English)
In climate science, models for global warming and weather prediction face significant challenges due to the limited availability of high-quality data and the difficulty in obtaining it, making data efficiency crucial. In the past few years, Scientific Machine Learning (SciML) models have gained tremendous traction as they can be trained in a data-efficient manner, making them highly suitable for real-world climate applications. Despite this, very little attention has been paid to chaotic climate system modeling utilizing SciML methods. In this paper, we have integrated SciML methods into foundational weather models, where we have enhanced large-scale climate predictions with a physics-informed approach that achieves high accuracy with reduced data. We successfully demonstrate that by combining the interpretability of physical climate models with the computational power of neural networks, SciML models can prove to be a reliable tool for modeling climate. This indicates a shift from the traditional black box-based machine learning modeling of climate systems to physics-informed decision-making, leading to effective climate policy implementation.
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