{"id":861624,"date":"2022-07-12T15:14:28","date_gmt":"2022-07-12T22:14:28","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-11-24T20:54:30","modified_gmt":"2022-11-25T04:54:30","slug":"machine-learning-nonconservative-dynamics-for-new-physics-detection","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/machine-learning-nonconservative-dynamics-for-new-physics-detection\/","title":{"rendered":"Machine-learning Nonconservative Dynamics for New Physics Detection"},"content":{"rendered":"<p>Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven &#8220;new physics&#8221; 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\u00a0<span id=\"MathJax-Element-1-Frame\" class=\"MathJax\"><span id=\"MathJax-Span-1\" class=\"math\"><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"mi\">\u03bb<\/span><\/span><\/span><\/span>\u00a0times the magnitude of the predicted non-conservative force. We show that a phase transition occurs at\u00a0<span id=\"MathJax-Element-2-Frame\" class=\"MathJax\"><span id=\"MathJax-Span-4\" class=\"math\"><span id=\"MathJax-Span-5\" class=\"mrow\"><span id=\"MathJax-Span-6\" class=\"mi\">\u03bb<\/span><\/span><\/span><\/span>=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&#8217; 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven &#8220;new physics&#8221; 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 [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Physical Review 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