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Updated: Jan 20, 2026

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VENI,VINDy,VICI:一个具有不确定性定量化的生成性减少顺序建模框架

Paolo Conti¹, Jonas Kneifl², Andrea Manzoni¹

¹MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy.

Neural networks : the official journal of the International Neural Network Society

|January 18, 2026

概括

我们为生成模型引入了一个新的框架,以确保科学预测的物理一致性. 这种方法将数据驱动的方法与概率建模集成为准确的,不确定性意识的减少顺序模型.

科学领域:

计算科学计算科学
基于物理的机器学习
数据驱动建模数据驱动建模

背景情况:

生成模型提供高效的场景探索,但往往缺乏物理一致性.
计算科学依赖于物理一致性来进行可靠的预测.
现有的模型难以平衡数据驱动的洞察力与物理定律.

研究的目的:

开发一种新的物理生成框架,用于创建物理一致的减少顺序模型.
将数据驱动的系统识别与不确定性量化的概率建模集成在一起.
通过确保模型可靠性,提高复杂物理现象的决策能力.

主要方法:

VENI (噪声输入的变量编码):使用变量自编码器来识别从高维,噪声数据中的缩小坐标.
VINDy (非线性动态的变量识别):将稀疏系统识别扩展到概率建模以发现系统动态.
VICI (具有可信度区间的变量推理):能够有效地生成全职解决方案,并提供不确定性量化.

主要成果:

拟议的框架成功地构建了物理一致的减少顺序模型.
证明了对未见的参数和初始条件的有效不确定性量化.
在各种系统中验证了性能,包括混乱和高维非线性动态.

结论:

VENI,VINDy,VICI框架为物理一致的生成建模提供了一个强大的解决方案.
这种方法提高了生成模型在科学和工程中的可靠性和适用性.
它为更加可靠和高效的复杂物理系统的计算探索铺平了道路.

关键词:

数据驱动的方法数据驱动的方法生成性AI是一种人工智能.非线性动力学是一种非线性动力学.减少的订单建模减少的订单建模稀少的系统识别标识.变化自动编码器的变化自动编码器

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