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  "body": "\n## Summary\n\nMultivariate approximation is the part of [[approximation theory|YPJSH79SHQ]] that approximates functions of several variables.\nIts tools include [[polynomials in several variables|07GNX8GCYP]], tensor product methods, [[radial basis functions|XZ33P3HBB9]], box splines, [[sparse grids|84BR7X6HET]] and neural networks.\nMany one-dimensional facts fail or change form here: for instance, interpolation by polynomials at arbitrary distinct points is no longer always possible.\nA central question is how the error depends on the dimension, which leads to the [[curse of dimensionality|YS424FTY4E]].\n\n## Explanations\n\n### L1 human\n\nA function of one variable is a curve, and a function of two variables is a surface; in many applications there are dozens or thousands of variables.\nMultivariate approximation asks how to replace such a function by a simpler one, such as a polynomial, that is close to it everywhere.\nThe number of simple building blocks grows quickly with the number of variables.\nFor example, polynomials of degree at most $2$ in one variable form a space of dimension $3$, but in $10$ variables they form a space of dimension $\\binom{12}{2} = 66$.\n",
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