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  2. Machine learning in physics - Wikipedia

    en.wikipedia.org/wiki/Machine_learning_in_physics

    A deep learning system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on an unpublished approach inspired by studies of visual cognition in infants. [ 40 ] [ 39 ] Other researchers have developed a machine learning algorithm that could discover sets of basic variables of various physical systems and ...

  3. Applications of quantum mechanics - Wikipedia

    en.wikipedia.org/wiki/Applications_of_quantum...

    Quantum physics is a branch of modern physics in which energy and matter are described at their most fundamental level, that of energy quanta, elementary particles, and quantum fields. Quantum physics encompasses any discipline concerned with systems that exhibit notable quantum-mechanical effects, where waves have properties of particles, and ...

  4. Levels of Processing model - Wikipedia

    en.wikipedia.org/wiki/Levels_of_Processing_model

    Conversely, deep processing (e.g., semantic processing) results in a more durable memory trace. [1] There are three levels of processing in this model. Structural processing, or visual, is when we remember only the physical quality of the word (e.g. how the word is spelled and how letters look).

  5. Physics-informed neural networks - Wikipedia

    en.wikipedia.org/wiki/Physics-informed_neural...

    Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).

  6. Perturbation theory (quantum mechanics) - Wikipedia

    en.wikipedia.org/wiki/Perturbation_theory...

    This is particularly useful in laser physics, where one is interested in the populations of different atomic states in a gas when a time-dependent electric field is applied. These probabilities are also useful for calculating the "quantum broadening" of spectral lines (see line broadening) and particle decay in particle physics and nuclear physics.

  7. Electromagnetically induced transparency - Wikipedia

    en.wikipedia.org/wiki/Electromagnetically...

    In any real system at non-zero temperature there are processes which cause a scrambling of the phase of the quantum states. In the gas phase, this means usually collisions. In solids, dephasing is due to interaction of the electronic states with the host lattice.

  8. AI accelerator - Wikipedia

    en.wikipedia.org/wiki/AI_accelerator

    An AI accelerator, deep learning processor or neural processing unit (NPU) is a class of specialized hardware accelerator [1] or computer system [2] [3] designed to accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision.

  9. Deep inelastic scattering - Wikipedia

    en.wikipedia.org/wiki/Deep_inelastic_scattering

    Feynman diagram representing deep inelastic scattering of a lepton (l) on a hadron (h), at leading order in perturbative expansion. The virtual photon (γ * ) knocks a quark (q) out of the hadron. In particle physics , deep inelastic scattering is the name given to a process used to probe the insides of hadrons (particularly the baryons , such ...