Science Alert July 29, 2022
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. Researchers at Columbia University proposed a principle for determining how many state variables an observed system is likely to have, and what these variables might be. They demonstrated the effectiveness of this approach using video recordings of a variety of physical dynamical systems, ranging from elastic double pendulums to fire flames. After being shown videos of physical phenomena on Earth, the AI didn’t rediscover the current variables they used; instead, it came up with new variables to explain what it saw. Without any prior knowledge of the underlying physics, their algorithm discovered the intrinsic dimension of the observed dynamics and identified candidate sets of state variables. According to the researchers their work suggests that in the future, AI could potentially help us to identify variables that underpin new concepts we are not currently aware of…read more. TECHNICAL ARTICLE
Two-stage modelling of dynamical systems… Credit: Nature Computational Science volume 2, pages433–442 (2022)Â