
A computational technique for approximating solutions to mathematical problems that lack closed-form solutions.Numerical methods are computational techniques used to find approximate solutions to mathematical problems that cannot be solved exactly, or where an exact solution is impractical. Many equations in physics, such as the Schrödinger equation for multi-electron atoms or the equations of general relativity for black hole mergers, have no closed-form solutions. Instead of seeking a perfect formula, numerical methods break the problem into many small, manageable steps and perform arithmetic on them, yielding a result that is accurate to a desired degree.
Numerical methods have roots in ancient approximations, but modern computational techniques emerged with the advent of electronic computers in the mid-20th century. Pioneers like John von Neumann and Alan Turing contributed to early algorithms for scientific computing.
The core idea is discretization: replacing continuous quantities with discrete values. For example, a differential equation might be approximated by a finite difference equation, where derivatives become differences over tiny intervals. The problem then becomes a large set of algebraic equations, which can be solved iteratively on a computer. Methods like finite element analysis, Monte Carlo simulations, and spectral methods each approach this discretization differently, trading off speed, accuracy, and stability.
Numerical methods are used in virtually all computational physics, from simulating galaxy formation to designing aircraft wings and predicting weather.
The first numerical weather forecast, run on the ENIAC computer in 1950, took 24 hours to compute a 24-hour forecast for a small region of the US.





