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This study introduces a neural network method for discovering new physics by separating forces into conservative and nonconservative parts. The approach successfully identified phenomena like friction and gravitational waves in simulations.

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Area of Science:

  • Physics
  • Machine Learning
  • Data Science

Background:

  • Energy conservation is a fundamental principle in physics.
  • Deviations from energy conservation often signal the presence of new physical phenomena.
  • Discovering new physics typically requires theoretical breakthroughs or complex experimental setups.

Purpose of the Study:

  • To present a novel data-driven method for detecting new physics.
  • To develop a neural network-based approach for decomposing force fields into conservative and nonconservative components.
  • To demonstrate the efficacy of this method in identifying known yet previously unexplained physical phenomena.

Main Methods:

  • A neural new-physics detector (NNPhD) was developed, utilizing a Lagrangian neural network (LNN) for conservative forces and an unconstrained neural network for nonconservative forces.
  • The networks were trained to minimize force recovery error, with a penalty on the magnitude of the nonconservative force component (controlled by parameter λ).
  • A universal phase transition at λ=1 was identified, indicating a critical point for new physics detection.

Main Results:

  • The NNPhD successfully rediscovered known physics, including friction in a damped double pendulum, Neptune's gravitational influence on Uranus' orbit, and gravitational waves from inspiraling orbits.
  • A phase transition at λ=1 was observed to be universal across different force fields.
  • When combined with an integrator, the NNPhD outperformed standalone LNN and unconstrained neural networks in predicting the future behavior of a damped double pendulum.

Conclusions:

  • The developed NNPhD offers a powerful data-driven tool for discovering new physics.
  • The method's ability to identify known phenomena validates its potential for uncovering currently unknown physical laws.
  • This approach advances the integration of machine learning with fundamental physics research, particularly in analyzing complex dynamical systems.