Computational physics machine learning

  • Can machine learning be used in physics?

    ML is already being used extensively in physics, which is unsurprising since physics deals with data that are often very large, as is the case in some high-energy physics and astrophysics experiments.
    In fact, physicists have been using some forms of ML for a long time, even before the term ML became popular..

  • However, a physics-based ML model integrates data, partial differential equations (PDEs), and mathematical models to solve data shift problems.
    Physics-based ML models are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear equations.
  • Physics simulations generate vast datasets that help AI systems learn and adapt in virtual environments.
    For example, AI-powered robots learn locomotion skills through simulations that adhere to the laws of physics, helping them transfer their knowledge to the real world.
Classical machine learning is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system,  Applications of machine Noisy dataCalculated and noise-free data

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