The Challenges of Approximating Functions of Many Variables

Function approximation is relatively simple compared to many other continuous numerical problems, such as solving (stochastic and/or partial) differential equations. Interpolation is often used in the case of noiseless data, and regression can handle the case of noisy data. For functions of one variable, collecting sufficient data is often straightforward, but for functions of many variables, the function must satisfy some simplifying structure for approximation to be successful. The problem is even more complicated when function values are costly, such as when some complex computer simulation generates them. This talk highlights some of the challenges of approximating functions of many variables and the strategies for overcoming these challenges.

Event Topic

Computational Mathematics & Statistics