从数据中学习动态系统:介绍物理引导的深度学习
1Department of Computer Science and Engineering, University of California, San Diego, CA 92093.
概括
物理指导的深度学习 (DL) 将物理定律集成到复杂动态的数据驱动模型中. 这种方法结合了传统基于物理的模型和DL的优势,提供了改进的科学问题解决.
科学领域:
- 科学建模的科学建模
- 动态系统是动态系统.
- 计算科学是一种计算科学.
背景情况:
- 传统的基于物理的模型提供了可解释性,但需要大量的资源和专业知识.
- 深度学习 (DL) 模型是高效的,但需要大量的数据,可能违反物理定律.
- 现有的方法在复杂的动态建模中努力平衡准确性,可解释性和数据效率.
研究的目的:
- 引入针对动态系统量身定制的物理引导深度学习 (DL) 的框架.
- 在这个框架内对最先进的方法进行分类和分析.
- 确定用于科学应用的物理引导DL的挑战和机遇.
主要方法:
- 将第一原则的物理知识纳入数据驱动的DL方法.
- 开发一个专门针对动态系统的学习管道.
- 对当前以物理为导向的DL技术进行系统审查和分类.
主要成果:
- 证明了物理引导DL的潜力,以克服传统和纯DL方法的局限性.
- 提供了现有的现场方法的结构化概述.
- 强调了将物理定律与数据驱动学习相结合的协同效益.
结论:
- 物理引导的DL为建模复杂的物理动态提供了一个强大的范式.
- 该框架有助于开发更可靠,可解释和高效的科学模型.
- 未来的研究方向包括解决这个快速发展的领域的挑战和探索新机遇.
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