Discovering Nonlinear Dynamics Through Scientific Machine Learning
Discovering Nonlinear Dynamics Through Scientific Machine Learning
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DOI：
10.1007/978-3-030-82193-7_17
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Lei Huang;D. Vrinceanu;Yunjiao Wang;N. Kulathunga;N. R. Ranasinghe
中科院分区：
文献类型：
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作者：
Lei Huang;D. Vrinceanu;Yunjiao Wang;N. Kulathunga;N. R. Ranasinghe
科学机器学习（SciML）是一种新的多学科方法，它结合了数据驱动的机器学习模型和基于原理的计算模型，以改进科学现象的模拟并从现有测量中发现新的科学规则。本文揭示了使用 SciML 方法发现现实场景中可能难以建模或未知的非线性动力学的经验。 SciML 方法通过集成神经网络来求解传统的基于原理的微分方程，以在尊重科学约束和原理的同时对非线性动力学进行精确建模。本文讨论了最新的 SciML 模型并将其应用于振荡器模拟和实验。除了更好的模拟能力以及与观测结果的匹配之外，结果还证明使用 SciML 成功发现了摆动力学中隐藏的物理现象。
Scientific Machine Learning (SciML) is a new multidisciplinary methodology that combines the data-driven machine learning models and the principle-based computational models to improve the simulations of scientific phenomenon and uncover new scientific rules from existing measurements. This article reveals the experience of using the SciML method to discover the nonlinear dynamics that may be hard to model or be unknown in the real-world scenario. The SciML method solves the traditional principle-based differential equations by integrating a neural network to accurately model the nonlinear dynamics while respecting the scientific constraints and principles. The paper discusses the latest SciML models and apply them to the oscillator simulations and experiment. Besides better capacity to simulate, and match with the observation, the results also demonstrate a successful discovery of the hidden physics in the pendulum dynamics using SciML.