Machine-Learning Non-Conservative Dynamics for New-Physics Detection
This addresses the challenge of discovering unknown physical laws from observational data, which is incremental as it builds on existing neural network approaches for physics modeling.
The paper tackles the problem of detecting new physics from trajectory data by decomposing forces into conservative and non-conservative components, showing that their method successfully identifies known phenomena like friction and gravitational waves in numerical experiments and improves prediction accuracy for damped systems.
Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven "new physics" discovery. Specifically, given a trajectory governed by unknown forces, our Neural New-Physics Detector (NNPhD) aims to detect new physics by decomposing the force field into conservative and non-conservative components, which are represented by a Lagrangian Neural Network (LNN) and a universal approximator network (UAN), respectively, trained to minimize the force recovery error plus a constant $λ$ times the magnitude of the predicted non-conservative force. We show that a phase transition occurs at $λ$=1, universally for arbitrary forces. We demonstrate that NNPhD successfully discovers new physics in toy numerical experiments, rediscovering friction (1493) from a damped double pendulum, Neptune from Uranus' orbit (1846) and gravitational waves (2017) from an inspiraling orbit. We also show how NNPhD coupled with an integrator outperforms previous methods for predicting the future of a damped double pendulum.