Kavli Affiliate: Max Tegmark

| First 5 Authors: Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark

| Summary:

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.

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