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 $\lambda$ times the magnitude of the predicted
non-conservative force. We show that a phase transition occurs at $\lambda$=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.

本论文提出了一种基于数据的新物理发现方法，使用神经新物理探测器（NNPhD）分解力场，将其表示为拉格朗日神经网络（LNN）和通用近似器网络（UAN），成功在玩具数值实验中发现了新物理，并演示了 NNPhD 如何与积分器结合，优于以前预测阻尼双摆未来的方法。