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  2. 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).

  3. Machine learning in physics - Wikipedia

    en.wikipedia.org/wiki/Machine_learning_in_physics

    e. Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. [1] Other examples include learning Hamiltonians, [2][3] learning quantum phase transitions, [4][5] and ...

  4. Physical symbol system - Wikipedia

    en.wikipedia.org/wiki/Physical_symbol_system

    A physical symbol system (also called a formal system) takes physical patterns (symbols), combining them into structures (expressions) and manipulating them (using processes) to produce new expressions. The physical symbol system hypothesis (PSSH) is a position in the philosophy of artificial intelligence formulated by Allen Newell and Herbert ...

  5. Computational physics - Wikipedia

    en.wikipedia.org/wiki/Computational_physics

    e. Computational physics is the study and implementation of numerical analysis to solve problems in physics. [1] Historically, computational physics was the first application of modern computers in science, and is now a subset of computational science. It is sometimes regarded as a subdiscipline (or offshoot) of theoretical physics, but others ...

  6. Symbolic artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Symbolic_artificial...

    e. In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) [1][2] is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. [3]

  7. Stanford Research Institute Problem Solver - Wikipedia

    en.wikipedia.org/wiki/Stanford_Research...

    The Stanford Research Institute Problem Solver, known by its acronym STRIPS, is an automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International. [1] The same name was later used to refer to the formal language of the inputs to this planner. This language is the base for most of the languages for expressing ...

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