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  2. Spring system - Wikipedia

    en.wikipedia.org/wiki/Spring_system

    A spring system can be thought of as the simplest case of the finite element method for solving problems in statics. Assuming linear springs and small deformation (or restricting to one-dimensional motion) a spring system can be cast as a (possibly overdetermined) system of linear equations or equivalently as an energy minimization problem.

  3. Hooke's law - Wikipedia

    en.wikipedia.org/wiki/Hooke's_law

    In physics, Hooke's law is an empirical law which states that the force (F) needed to extend or compress a spring by some distance (x) scales linearly with respect to that distance—that is, F s = kx, where k is a constant factor characteristic of the spring (i.e., its stiffness), and x is small compared to the total possible deformation of the spring.

  4. Mass-spring-damper model - Wikipedia

    en.wikipedia.org/wiki/Mass-spring-damper_model

    Classic model used for deriving the equations of a mass spring damper model. The mass-spring-damper model consists of discrete mass nodes distributed throughout an object and interconnected via a network of springs and dampers. This model is well-suited for modelling object with complex material properties such as nonlinearity and viscoelasticity.

  5. Effective mass (spring–mass system) - Wikipedia

    en.wikipedia.org/wiki/Effective_mass_(spring...

    The effective mass of the spring in a spring-mass system when using a heavy spring (non-ideal) of uniform linear density is of the mass of the spring and is independent of the direction of the spring-mass system (i.e., horizontal, vertical, and oblique systems all have the same effective mass). This is because external acceleration does not ...

  6. 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).

  7. MOOSE (software) - Wikipedia

    en.wikipedia.org/wiki/MOOSE_(software)

    A Kernel is a "piece" of physics. To add new physics to an application built using MOOSE, all that is required is to supply a new Kernel that describes the discrete form of the equation. It's usually convenient to think of a Kernel as a mathematical operator, such as a Laplacian or a convection term in a partial differential equation (PDE ...

  8. Who will Steelers play in NFL playoffs? Scenarios, potential ...

    www.aol.com/steelers-play-nfl-playoffs-scenarios...

    The Steelers are headed to the playoffs, but who will they play? Here's a look at Pittsburgh's potential playoff opponents.

  9. Mechanical–electrical analogies - Wikipedia

    en.wikipedia.org/wiki/Mechanical–electrical...

    In an electrical network diagram, limited to linear systems, there are three passive elements: resistance, inductance, and capacitance; and two active elements: the voltage generator, and the current generator. [note 2] The mechanical analogs of these elements can be used to construct a mechanical network diagram. What the mechanical analogs of ...