Constants of Motion for Conserved and Non-conserved Dynamics
This provides a method to extract multiple constants of motion from single data sets, which is incremental for researchers in dynamical systems and physics-informed machine learning.
The paper tackled the problem of finding constants of motion from data-driven dynamical models, using Lie symmetry techniques on harmonic oscillators, and identified constants for both conserved and non-conserved cases, including novel constants for non-conserved systems interpreted as total energy conservation.
This paper begins with a dynamical model that was obtained by applying a machine learning technique (FJet) to time-series data; this dynamical model is then analyzed with Lie symmetry techniques to obtain constants of motion. This analysis is performed on both the conserved and non-conserved cases of the 1D and 2D harmonic oscillators. For the 1D oscillator, constants are found in the cases where the system is underdamped, overdamped, and critically damped. The novel existence of such a constant for a non-conserved model is interpreted as a manifestation of the conservation of energy of the {\em total} system (i.e., oscillator plus dissipative environment). For the 2D oscillator, constants are found for the isotropic and anisotropic cases, including when the frequencies are incommensurate; it is also generalized to arbitrary dimensions. In addition, a constant is identified which generalizes angular momentum for all ratios of the frequencies. The approach presented here can produce {\em multiple} constants of motion from a {\em single}, generic data set.