Automated discovery of fundamental variables hidden in experimental data
Automated discovery of fundamental variables hidden in experimental data
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DOI：
10.1038/s43588-022-00281-6
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发表时间：
2022-07-01
期刊：
影响因子：
--
通讯作者：
Lipson, Hod
中科院分区：
文献类型：
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作者：
Chen, Boyuan;Huang, Kuang;Lipson, Hod
所有的物理定律都被描述为状态变量之间的数学关系。这些变量给出了相关系统的完整和非冗余描述。然而，尽管计算能力和人工智能盛行，但识别隐藏状态变量本身的过程却抵制了自动化。大多数数据驱动的物理现象建模方法仍然依赖于相关状态变量已知的假设。一个长期存在的问题是，是否有可能只从高维观测数据中识别状态变量。在这里，我们提出了一个原则，确定有多少状态变量的观测系统可能有，这些变量可能是什么。我们证明了这种方法的有效性，使用视频记录的各种物理动力系统，从弹性双双锥火火焰。没有任何先验知识的基础物理，我们的算法发现所观察到的动态的内在维度，并确定候选集的状态变量。
All physical laws are described as mathematical relationships between state variables. These variables give a complete and non-redundant description of the relevant system. However, despite the prevalence of computing power and artificial intelligence, the process of identifying the hidden state variables themselves has resisted automation. Most data-driven methods for modelling physical phenomena still rely on the assumption that the relevant state variables are already known. A longstanding question is whether it is possible to identify state variables from only high-dimensional observational data. Here we propose a principle for determining how many state variables an observed system is likely to have, and what these variables might be. We demonstrate the effectiveness of this approach using video recordings of a variety of physical dynamical systems, ranging from elastic double pendulums to fire flames. Without any prior knowledge of the underlying physics, our algorithm discovers the intrinsic dimension of the observed dynamics and identifies candidate sets of state variables.