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Modeling photon propagation with Monte Carlo methods is a flexible yet rigorous approach to simulate photon transport. In the method, local rules of photon transport are expressed as probability distributions which describe the step size of photon movement between sites of photon-matter interaction and the angles of deflection in a photon's trajectory when a scattering event occurs.
Event bubbling is a type of DOM event propagation [1] where the event first triggers on the innermost target element, and then successively triggers on the ancestors (parents) of the target element in the same nesting hierarchy till it reaches the outermost DOM element or document object [2] (Provided the handler is initialized). It is one way ...
An example of usage of this method is in the field of light pulse propagation in optical fibers, where the interaction of linear and nonlinear mechanisms makes it difficult to find general analytical solutions. However, the split-step method provides a numerical solution to the problem.
event.stopPropagation(): the event is stopped after all event listeners attached to the current event target in the current event phase are finished; event.stopImmediatePropagation(): the event is stopped immediately and no further event listeners are executed; When an event is stopped it will no longer travel along the event path.
Propagation of a ray through a layer. The transfer-matrix method is a method used in optics and acoustics to analyze the propagation of electromagnetic or acoustic waves through a stratified medium; a stack of thin films. [1] [2] This is, for example, relevant for the design of anti-reflective coatings and dielectric mirrors.
Comparison of Euler's and Leapfrog integration energy conserving properties for N bodies orbiting a point source mass. Same time-step used in both simulations. The method is known by different names in different disciplines. In particular, it is similar to the velocity Verlet method, which is a variant of Verlet integration.
"A Runge-Kutta Fehlberg method with phase-lag of order infinity for initial-value problems with oscillating solution". Computers & Mathematics with Applications . 25 (6): 95– 101.
The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters of the model curve (,) so that the sum of the squares of the deviations () is minimized: