Computational physics

Computational physics is a branch of physics that focuses on computer modeling of systems with many degrees of freedom for which a computational theory already exists. . In general, microscopic models are made in which the "particles" they obey a simplified dynamics, and it is studied whether the macroscopic properties can be reproduced from this very simple model of the constituent parts. Simulations are made by solving equations that govern the system. They are usually large systems of ordinary differential equations, partial differential equations, and stochastic differential equations, which cannot be explicitly solved analytically.

Often, the simplified dynamics of "particles" has some degree of randomness. In general, this aspect is called the Monte Carlo method, a name that comes from the Monte Carlo casinos as a joking way of remembering that the method uses randomness.

Other simulations are based on the fact that the evolution of a "particle" in the system it depends exclusively on the state of the neighboring particles, and is governed by very simple and, in principle, determined rules. This is called simulations with cellular automata. A classic example, though more mathematical than physical, is the famous game of life, devised by John Horton Conway.

Computational physics has its most relevant applications in solid state physics (magnetism, electronic structure, molecular dynamics, phase changes, etc.), nonlinear physics, fluid dynamics, astrophysics (simulations of the Solar System, for example). example), particle physics (field theory/gauge theory in a space-time lattice, especially for Quantum Chromodynamics (QCD)).

The simulations that are carried out in computational physics require great calculation capacity, which is why in many cases it is necessary to use supercomputers or clusters of computers in parallel.