Organizing a global competition between approximation methods used for analyzing and modeling large spatial datasets enabled KAUST researchers to compare the performance of these different methods.

Approximation theory and asymptotic methods form a foundational framework that bridges classical ideas with modern numerical analysis, enabling researchers to obtain practical, near‐optimal solutions ...

Celebrate Pi Day with the best fractional approximation of Pi using Python! In this video, I’ll show you how to generate the most accurate rational approximations for Pi, diving into algorithms like ...

Dynamical low-rank approximation (DLRA) methods have emerged as a powerful numerical framework for addressing the challenges posed by high-dimensional problems. By restricting the evolution of a ...

Covers asymptotic evaluation of integrals (stationary phase and steepest descent), perturbation methods (regular and singular methods, and inner and outer expansions), multiple scale methods, and ...

Clinical trials have traditionally followed a fixed design, in which patient allocation to treatments is fixed throughout the trial and specified in the protocol. The primary goal of this static ...

Journal of Computational Mathematics, Vol. 24, No. 3, SPECIAL ISSUE DEDICATED TO THE 70TH BIRTHDAY OF PROFESSOR LIN QUN (MAY 2006), pp. 365-372 (8 pages) In this ...

Future events such as the weather or satellite trajectories are computed in tiny time steps, so the computation must be both efficient and as accurate as possible at each step lest errors pile up. A ...

This is a preview. Log in through your library . SIAM Journal on Numerical Analysis contains research articles on the development and analysis of numerical methods ...

In this topic we will advance the fundamental mathematical understanding of artificial neural networks, e.g., through the design and rigorous analysis of stochastic gradient descent methods for their ...