Machine-Learning Non-Conservative Dynamics for New-Physics Detection

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.

| Search Query: ArXiv Query: search_query=au:”Max Tegmark”&id_list=&start=0&max_results=10

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