Quantitative physics involves repeated measurements and mathematical analysis of experimental results to draw conclusions from statistical analysis of large amounts of data. It uses mathematics to understand the forces at work in the universe and is important in physics because physical laws cannot be defined quantitatively by human observation alone. Computational physics complements quantitative physics in areas where complex interactions occur.
Quantitative physics is the branch of physics that involves research by repeated measurements and mathematical analysis of experimental results. It differs from some branches of theoretical physics, for example, such as quantum mechanics or string theory research, where much of the underlying theory cannot be tested in the real world or in a laboratory on Earth with current technology as of 2011. Any field of quantitative research such as quantitative physics draws its conclusions from a statistical analysis of large amounts of experimental data. This data is often so vast and complex, however, that computers are used to run mathematical models of the data to better interpret it. An example of the use of quantitative physics would include climate studies performed on supercomputers to predict climate change due to various natural thermodynamic forces at play on, in, or near the Earth, as well as from changes in solar activity over long periods of time. .
The study of physics at its core is the measurement of changes in matter and energy, and this causes most physics to seek quantitative physics in one form or another. Quantitative study is also important in physics because many of the physical laws, such as the speed of light or the gravitational pull of the Earth, cannot be defined quantitatively by human observation with the five senses alone. You can watch a falling body, but without accurately measuring its rate of descent, you don’t get a clear picture of how strong gravity actually is. Quantitative research physics, therefore, uses mathematics as an abstract way of understanding the forces at work in the universe.
Processes involving quantitative study, however, are not always intended to represent everyday reality. Physics determines the ideal conditions under which matter, energy, space and time interact through repeated measurements and observations, then determines the probability that events will occur. The physical equations used for this are based on abstract mathematical concepts that only prove true with a large number of repeated experiments. Quantitative physics, for example, can predict the surface area of a spherical planet in space, but there is no perfect sphere or any other perfect geometric shape in the natural world, so the process is, to some extent, an approximation .
Ideal representations in physics, such as the ballistic trajectory of a projectile through air, are based on the quantitative physics principles of gravitational attraction and air resistance, but can only predict a general trajectory for a projectile, not the point actual and precise on which it will land. The use of equations and formulas in quantitative physics often involves averaging some of the variables that come into play or using mathematical shortcuts to negate their effect on the equation. This is because the goal is to understand the laws of nature in principle versus those of specific, random applications.
Computational physics often complements quantitative physics in the laboratory, where equations cannot be formally or adequately tested in real-world experiments. Algorithms are often used to simplify such calculations. Algorithms are a set of mathematical rules that the computer uses to reduce the number of calculations required to solve a problem down to a finite series of steps. Computer assistance for quantitative physics is typically employed in areas where very complex interactions occur, such as materials science, nuclear accelerator research, and molecular dynamics in biology.
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