- "Computational physics is the study and implementation of numerical analysis to solve problems in physics."
This subfield focuses on the development of algorithms and data structures for solving physical problems, such as simulating the behavior of particles or modeling fluid dynamics.
Mathematical Methods: This includes calculus, linear algebra, differential equations, probability theory, and statistics, which are crucial for computational physics applications.
Computer Science Fundamentals: This involves computer architecture, operation systems, programming languages, and data structures.
Numerical Methods: This covers interpolation, approximate differentiation and integration, numerical linear algebra, Monte-Carlo methods, optimization techniques, and numerical solution of non-linear equations.
Parallel Computation: This involves algorithms that utilize parallel processing on supercomputers, clusters, or grids to solve computationally-demanding problems more efficiently.
Object-Oriented Programming: This programming approach supports encapsulation, inheritance, and polymorphism. It is crucial to computational physics software design to create efficient, maintainable, and reusable code.
Simulation Techniques: This involves Monte-Carlo simulations, molecular dynamics, and other statistical mechanics simulations to simulate complex physical systems.
Computational Fluid Dynamics: This field of study involves using computational methods to study and analyze fluid mechanics and related problems.
High-Performance Computing: This is the use of advanced hardware and software in complex simulations that require massive computing power.
Visualization: This involves creating graphical representations of data to analyze large data sets, explore complex systems, and make them more accessible to others.
Quantum Computing: This involves studying computation with quantum computers and quantum algorithms to develop new ways of simulating physical systems.
Molecular dynamics: Simulation of molecular motion using numerical integration algorithms for the equations of motion of classical mechanics.
Monte Carlo Methods: A broad class of computational algorithms that rely on repeated random sampling to compute numerical results.
Finite Element Method (FEM): Used to approximately solve differential equations in physics such as structural analysis, heat transfer, fluid flow, and electromagnetic potential.
Computational Fluid Dynamics (CFD): Numerical simulation of fluid motion and flow, often involving the solution of the Navier-Stokes equations for fluid motion.
Lattice Boltzmann Method (LBM): A technique for simulating fluid flow, based on simulating the motion of fluid particles on a grid.
Molecular dynamics simulations of biological systems: Computational techniques for simulating the movements of biological molecules such as proteins and nucleic acids.
Density Functional Theory (DFT): Method for solving the many-body Schrödinger equation of quantum mechanics using the density of electrons.
Computational astrophysics: Application of computational techniques to problems in astrophysics and cosmology, such as modeling the formation of galaxies or simulating the behavior of black holes.
High-performance computing: The use of computer hardware and software to solve complex problems in physics quickly and efficiently.
Quantum Monte Carlo: A family of methods for solving problems in quantum mechanics using Monte Carlo methods.
Numerical relativity: Using numerical analysis and computational techniques to simulate the behavior of gravitational systems, including black holes and other dense objects.
Quantum simulation: Using computational systems to model quantum mechanical phenomena or simulate quantum mechanical systems.
Computational materials science: The use of computational methods to predict the properties of materials, including the behavior of atoms and molecules and the structure of materials.
Computational optics: The use of computational methods to simulate the behavior of electromagnetic radiation, including light and other forms of radiation.
Computational biophysics: The use of computational methods to understand the behavior of biological systems, including the simulation of protein folding and genetic interactions.
- "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 consider it an intermediate branch between theoretical and experimental physics — an area of study which supplements both theory and experiment."
- "...the study and implementation of numerical analysis to solve problems in physics."
- "The study and implementation of numerical analysis to solve problems in physics."
- "Historically, computational physics was the first application of modern computers in science..."
- "...is now a subset of computational science."
- "...sometimes regarded as a subdiscipline (or offshoot) of theoretical physics..."
- "...an area of study which supplements both theory and experiment."
- "The study and implementation of numerical analysis to solve problems in physics."
- "The study and implementation of numerical analysis to solve problems in physics."
- "The first application of modern computers in science..."
- "...subset of computational science."
- "...an intermediate branch between theoretical and experimental physics..."
- "...a subdiscipline (or offshoot) of theoretical physics..."
- "...an intermediate branch between theoretical and experimental physics..."
- "The first application of modern computers in science..."
- "The first application of modern computers in science..."