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  2. List of rail transport modelling scale standards - Wikipedia

    en.wikipedia.org/wiki/List_of_rail_transport...

    North America specific scale corresponding to NMRA 1-inch scale. 1:12 is one of the most popular backyard railway scales. -1:11: 5 in (127 mm) Used outside North America. Corresponds to NEM V. One of the most popular garden railway scales. Common gauge for live steam-1:8: 7 + 1 ⁄ 4 in (184 mm)

  3. Rail transport modelling scales - Wikipedia

    en.wikipedia.org/.../Rail_transport_modelling_scales

    Fn3 scale, together with G scale and 12 inch (1:24) scale, are commonly and collectively referred to as "Large Scale" by many modelers. Fn2 scale: 1:20.3: 30 mm or 32 mm Used by mostly American modelers wishing to model smaller industrial prototypes, including two-footers; this is a minority scale.

  4. List of scale model sizes - Wikipedia

    en.wikipedia.org/wiki/List_of_scale_model_sizes

    1:150: 2.032 mm: Model railways (Japanese N) Used by Heller for model ships, and proposed by the Japanese to supersede 1:144 scale trains. Models which are commonly made in scale at 1:150 are commercial airliners - such as the Airbus A320, Boeing 777 all the way to the jumbo jets - the Airbus A380 & Boeing 747. [8] 1:148: 2.059 mm

  5. Category:Scale model scales - Wikipedia

    en.wikipedia.org/wiki/Category:Scale_model_scales

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more

  6. Scale model - Wikipedia

    en.wikipedia.org/wiki/Scale_model

    A scale model of the Tower of London. This model can be found inside the tower. A scale model of a hydropower turbine. A scale model is a physical model that is geometrically similar to an object (known as the prototype). Scale models are generally smaller than large prototypes such as vehicles, buildings, or people; but may be larger than ...

  7. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, [1] [2] and training cost.

  8. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    Also known as min-max scaling or min-max normalization, rescaling is the simplest method and consists in rescaling the range of features to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data. The general formula for a min-max of [0, 1] is given as: [3]

  9. Direct numerical simulation - Wikipedia

    en.wikipedia.org/wiki/Direct_numerical_simulation

    A direct numerical simulation (DNS) [1] [2] is a simulation in computational fluid dynamics (CFD) in which the Navier–Stokes equations are numerically solved without any turbulence model. This means that the whole range of spatial and temporal scales of the turbulence must be resolved.

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