从方程和数据中进行非线性模型缩小
Cecilia Pagliantini1, Shobhit Jain2
1Department of Mathematics, University of Pisa, Pisa, 56127, Italy.
Chaos (Woodbury, N.Y.)
|September 30, 2024
概括
科学和工程中的复杂模型很难模拟. 非线性模型还原为分析高维系统和数据提供了一个有希望的解决方案,使更好的预测和控制成为可能.
科学领域:
- 应用科学和工程应用科学和工程
- 计算科学是一种计算科学.
- 数据科学是数据科学.
背景情况:
- 科学和工程中的高维模型带来了重大的计算挑战.
- 尽管技术可行,但传统的模拟可能无法提供明确的见解.
- 数据驱动系统需要专门的建模方法.
研究的目的:
- 调查非线性模型减少的最新趋势.
- 探索在方程和数据集中的应用.
- 涵盖模型缩小的计算和理论方面.
主要方法:
- 专注于非线性模型的减少技术.
- 分析适用于数学方程和经验数据的方法.
- 审查该领域最近的进展.
主要成果:
- 减少顺序模型提供了对参数变化和不确定性的有效评估.
- 模型缩小有助于有效预测和控制复杂的系统.
- 最新的趋势包括各种应用和理论发展.
结论:
- 非线性模型缩小对于处理现代科学和工程中的复杂性至关重要.
- 这些技术对于由数据定义的系统至关重要.
- 该领域继续发展,带来新的计算和理论见解.
相关概念视频
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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