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Big Data & Computational Physics
Big Data & Computational Physics
Published: 2016/03/23
Channel: Ryan Bury
Computational Physics Movie
Computational Physics Movie
Published: 2012/10/12
Channel: Kevin Berwick
Career in Computational physics HD
Career in Computational physics HD
Published: 2017/05/20
Channel: Pakistan Career TV
C++ Multi-dimensional Arrays for Computational Physics and Applied Mathematics
C++ Multi-dimensional Arrays for Computational Physics and Applied Mathematics
Published: 2016/09/11
Channel: TechEd North America
Computational Physics Lecture 2, Introduction to Python
Computational Physics Lecture 2, Introduction to Python
Published: 2017/01/16
Channel: Ernazar Abdikamalov
Computational Physics Lecture 3, Introduction to Python
Computational Physics Lecture 3, Introduction to Python
Published: 2017/01/21
Channel: Ernazar Abdikamalov
Pramod Gupta - Computational Physics with Python: Planetary Orbits from Newton to Feynman
Pramod Gupta - Computational Physics with Python: Planetary Orbits from Newton to Feynman
Published: 2016/06/01
Channel: PyCon 2016
Big Data & Computational Physics
Big Data & Computational Physics
Published: 2016/04/04
Channel: Ryan Bury
Lec 1 | MIT 18.085 Computational Science and Engineering I, Fall 2008
Lec 1 | MIT 18.085 Computational Science and Engineering I, Fall 2008
Published: 2009/02/25
Channel: MIT OpenCourseWare
Reflections on Teaching Computational Physics and Mathematics
Reflections on Teaching Computational Physics and Mathematics
Published: 2016/12/21
Channel: Wolfram
An Introduction to Computational Multiphysics II: Theoretical Background Part I
An Introduction to Computational Multiphysics II: Theoretical Background Part I
Published: 2011/01/05
Channel: Harvard University
Use of HPC in Computational Physics
Use of HPC in Computational Physics
Published: 2014/11/11
Channel: Tennessee Supercomputing
Computational Physics Lecture 26, Introduction to Partial Differential Equations.
Computational Physics Lecture 26, Introduction to Partial Differential Equations.
Published: 2017/04/19
Channel: Ernazar Abdikamalov
Python Physics Simulation: Beauitful Bouncing Balls
Python Physics Simulation: Beauitful Bouncing Balls
Published: 2013/06/25
Channel: ForwardSlashReality
Computational Physics An Introduction
Computational Physics An Introduction
Published: 2017/05/10
Channel: Jacob Martinez
Undergraduate Computational Physics Competencies Presentation
Undergraduate Computational Physics Competencies Presentation
Published: 2016/03/31
Channel: Chair SIGHPC Education
Howard University Computational Physics Lab Introduction
Howard University Computational Physics Lab Introduction
Published: 2013/08/22
Channel: Howard University Computational Physics Laboratory
Computational Physics Science, Lecture 1, Landau
Computational Physics Science, Lecture 1, Landau
Published: 2008/10/16
Channel: rubinhlandau
Honors Computational Physics
Honors Computational Physics
Published: 2017/03/13
Channel: NCSSMDistanceEd
Computational Physics Lecture 5, Round-off and Truncation Errors
Computational Physics Lecture 5, Round-off and Truncation Errors
Published: 2017/01/28
Channel: Ernazar Abdikamalov
Computational Physics Lecture 4, Introduction to Matplotlib
Computational Physics Lecture 4, Introduction to Matplotlib
Published: 2017/01/22
Channel: Ernazar Abdikamalov
Basic Concepts in Computational Physics
Basic Concepts in Computational Physics
Published: 2016/04/25
Channel: SpringerVideos
Computational Phenomena in Physics | Scott Aaronson
Computational Phenomena in Physics | Scott Aaronson
Published: 2014/12/10
Channel: aoflex
Computational Physics Lecture 20, Numerical Integration I
Computational Physics Lecture 20, Numerical Integration I
Published: 2017/03/28
Channel: Ernazar Abdikamalov
Computational Physics FUN with Tom and Danielle
Computational Physics FUN with Tom and Danielle
Published: 2013/12/11
Channel: Danielle Weiland
Computational Physics Video 4 - Matrices, arrays and indexing in MATLAB
Computational Physics Video 4 - Matrices, arrays and indexing in MATLAB
Published: 2016/01/11
Channel: Hywel Owen
Computational Physics Lecture 22, Numerical Integration of ODEs
Computational Physics Lecture 22, Numerical Integration of ODEs
Published: 2017/03/29
Channel: Ernazar Abdikamalov
Computational Phenomena in Physics - Scott Aaronson
Computational Phenomena in Physics - Scott Aaronson
Published: 2016/01/06
Channel: Institute for Advanced Study
Computational Physics Lecture 6, Root Finding and Bracketing Methods.
Computational Physics Lecture 6, Root Finding and Bracketing Methods.
Published: 2017/01/30
Channel: Ernazar Abdikamalov
The Role of Computational Physics in Nanotechnology Research
The Role of Computational Physics in Nanotechnology Research
Published: 2016/07/07
Channel: Universidad YachayTech
Computational Physics Lecture 24, Implicit Euler Method and Stiff ODEs.
Computational Physics Lecture 24, Implicit Euler Method and Stiff ODEs.
Published: 2017/04/15
Channel: Ernazar Abdikamalov
A Computational Perspective on Quantum Physics
A Computational Perspective on Quantum Physics
Published: 2011/05/09
Channel: NCState
Computational Physics Problem Solving with Python
Computational Physics Problem Solving with Python
Published: 2016/03/26
Channel: Robert Modica
Computational physics
Computational physics
Published: 2017/06/12
Channel: WikiTV
Porting Computational Physics Applications to the Titan Supercomputer with OpenACC and OpenMP
Porting Computational Physics Applications to the Titan Supercomputer with OpenACC and OpenMP
Published: 2015/05/27
Channel: RichReport
Computational Physics Lecture 17, Interpolation
Computational Physics Lecture 17, Interpolation
Published: 2017/03/01
Channel: Ernazar Abdikamalov
Computational Physics Video 31 - Writing a Monte Carlo Radiation Transport Code
Computational Physics Video 31 - Writing a Monte Carlo Radiation Transport Code
Published: 2016/03/09
Channel: Hywel Owen
Computational Physics Video 18 - Euler
Computational Physics Video 18 - Euler's Method in Mathematica
Published: 2016/01/15
Channel: Hywel Owen
Computational Physics Lecture 21, Romberg Integration and Gauss Quadrature
Computational Physics Lecture 21, Romberg Integration and Gauss Quadrature
Published: 2017/03/29
Channel: Ernazar Abdikamalov
CppCon 2015: Pramod Gupta “C++ Multi-dimensional Arrays...”
CppCon 2015: Pramod Gupta “C++ Multi-dimensional Arrays...”
Published: 2015/10/08
Channel: CppCon
Computational Physics Lecture 18, Fourier Series and Transform
Computational Physics Lecture 18, Fourier Series and Transform
Published: 2017/03/11
Channel: Ernazar Abdikamalov
Computational physics final
Computational physics final
Published: 2013/12/13
Channel: Michael Moen
Download An Introduction to Computational Physics PDF
Download An Introduction to Computational Physics PDF
Published: 2017/01/20
Channel: Raksi
Computational Physics Problem Solving with Python
Computational Physics Problem Solving with Python
Published: 2017/02/24
Channel: Olga Rzhevskaya
Computational Physics (CHEP
Computational Physics (CHEP's first Annual Dinner 22-Dec-05)
Published: 2008/06/05
Channel: hafizkhaleeq
Computational Physics Lecture 19, Discrete Fourier Transform and Fast Fourier Transform
Computational Physics Lecture 19, Discrete Fourier Transform and Fast Fourier Transform
Published: 2017/03/15
Channel: Ernazar Abdikamalov
Books of Computational Physics Problem Solving with Python
Books of Computational Physics Problem Solving with Python
Published: 2015/10/21
Channel: Marguerite Calla
ย้อนรอย Computational Physics@KKU - สงกรานต์ 53
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Published: 2014/09/13
Channel: teepanis
Computational Physics Problem No. 1 (Objects
Computational Physics Problem No. 1 (Objects' equilibrium)
Published: 2016/02/21
Channel: Physics Pantheon
Computational Physics Lecture 23, Runge-Kutta Methods
Computational Physics Lecture 23, Runge-Kutta Methods
Published: 2017/04/01
Channel: Ernazar Abdikamalov
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WIKIPEDIA ARTICLE

From Wikipedia, the free encyclopedia
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Computational physics is the study and implementation of numerical analysis to solve problems in physics for which a quantitative theory already exists.[1] 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, a third way that supplements theory and experiment.[2]

Overview[edit]

A representation of the multidisciplinary nature of computational physics both as an overlap of physics, applied mathematics, and computer science and as a bridge among them.[3]

In physics, different theories based on mathematical models provide very precise predictions on how systems behave. Unfortunately, it is often the case that solving the mathematical model for a particular system in order to produce a useful prediction is not feasible. This can occur, for instance, when the solution does not have a closed-form expression, or is too complicated. In such cases, numerical approximations are required. Computational physics is the subject that deals with these numerical approximations: the approximation of the solution is written as a finite (and typically large) number of simple mathematical operations (algorithm), and a computer is used to perform these operations and compute an approximated solution and respective error.[1]

Status in physics[edit]

There is a debate about the status of computation within the scientific method.[4]

Sometimes it is regarded as more akin to theoretical physics; some others regard computer simulation as "computer experiments",[4] yet still others consider it an intermediate or different branch between theoretical and experimental physics, a third way that supplements theory and experiment. While computers can be used in experiments for the measurement and recording (and storage) of data, this clearly does not constitute a computational approach.

Challenges in computational physics[edit]

Physics problems are in general very difficult to solve exactly. This is due to several (mathematical) reasons: lack of algebraic and/or analytic solubility, complexity, and chaos.

For example, - even apparently simple problems, such as calculating the wavefunction of an electron orbiting an atom in a strong electric field (Stark effect), may require great effort to formulate a practical algorithm (if one can be found); other cruder or brute-force techniques, such as graphical methods or root finding, may be required. On the more advanced side, mathematical perturbation theory is also sometimes used (a working is shown for this particular example here).

In addition, the computational cost and computational complexity for many-body problems (and their classical counterparts) tend to grow quickly. A macroscopic system typically has a size of the order of constituent particles, so it is somewhat of a problem. Solving quantum mechanical problems is generally of exponential order in the size of the system[citation needed] and for classical N-body it is of order N-squared.

Finally, many physical systems are inherently nonlinear at best, and at worst chaotic: this means it can be difficult to ensure any numerical errors do not grow to the point of rendering the 'solution' useless.[5]

Methods and algorithms[edit]

Because computational physics uses a broad class of problems, it is generally divided amongst the different mathematical problems it numerically solves, or the methods it applies. Between them, one can consider:

All these methods (and several others) are used to calculate physical properties of the modeled systems.

Computational physics also borrows a number of ideas from computational chemistry - for example, the density functional theory used by computational solid state physicists to calculate properties of solids is basically the same as that used by chemists to calculate the properties of molecules.

Furthermore, computational physics encompasses the tuning of the software/hardware structure to solve the problems (as the problems usually can be very large, in processing power need or in memory requests).

Divisions[edit]

It is possible to find a corresponding computational branch for every major field in physics, for example computational mechanics and computational electrodynamics. Computational mechanics consists of computational fluid dynamics (CFD), computational solid mechanics and computational contact mechanics. One subfield at the confluence between CFD and electromagnetic modelling is computational magnetohydrodynamics. The quantum many-body problem leads naturally to the large and rapidly growing field of computational chemistry.

Computational solid state physics is a very important division of computational physics dealing directly with material science.

A field related to computational condensed matter is computational statistical mechanics, which deals with the simulation of models and theories (such as percolation and spin models) that are difficult to solve otherwise. Computational statistical physics makes heavy use of Monte Carlo-like methods. More broadly, (particularly through the use of agent based modeling and cellular automata) it also concerns itself with (and finds application in, through the use of its techniques) in the social sciences, network theory, and mathematical models for the propagation of disease (most notably, the SIR Model) and the spread of forest fires.

On the more esoteric side, numerical relativity is a (relatively) new field interested in finding numeric solutions to the field equations of general (and special) relativity, and computational particle physics deals with problems motivated by particle physics.

Computational astrophysics is the application of these techniques and methods to astrophysical problems and phenomena.

Applications[edit]

Due to the broad class of problems computational physics deals, it is an essential component of modern research in different areas of physics, namely: accelerator physics, astrophysics, fluid mechanics (computational fluid dynamics), lattice field theory/lattice gauge theory (especially lattice quantum chromodynamics), plasma physics (see plasma modeling), simulating physical systems (using e.g. molecular dynamics), protein structure prediction, weather prediction, solid state physics, soft condensed matter physics, hypervelocity impact physics etc.

Computational solid state physics, for example, uses density functional theory to calculate properties of solids, a method similar to that used by chemists to study molecules. Other quantities of interest in solid state physics, such as the electronic band structure, magnetic properties and charge densities can be calculated by this and several methods, including the Luttinger-Kohn/k.p method and ab-initio methods.

See also[edit]

References[edit]

  1. ^ a b Thijssen, Joseph (2007). Computational Physics. Cambridge University Press. ISBN 0521833469. 
  2. ^ Landau, Rubin H.; Páez, Manuel J.; Bordeianu, Cristian C. (2015). Computational Physics: Problem Solving with Python. John Wiley & Sons. 
  3. ^ Landau, Rubin H.; Paez, Jose; Bordeianu, Cristian C. (2011). A survey of computational physics: introductory computational science. Princeton University Press. 
  4. ^ a b A molecular dynamics primer, Furio Ercolessi, University of Udine, Italy. Article PDF.
  5. ^ "How Long Do Numerical Chaotic Solutions Remain Valid?". Bibcode:1997PhRvL..79...59S. doi:10.1103/PhysRevLett.79.59. 

Further reading[edit]

External links[edit]

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