In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

The immune system is a remarkable information processing and self learning system that
offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a …

We formulate a general framework for hp-variational physics-informed neural networks (hp-
VPINNs) based on the nonlinear approximation of shallow and deep neural networks and …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

This book describes the new generation of discrete choice methods, focusing on the many
advances that are made possible by simulation. Researchers use these statistical methods …

The seventh edition of this classic text outlines the fundamental physical principles of
thermal radiation, as well as analytical and numerical techniques for quantifying radiative …

Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial
differential equations, generate digital twins, and create neural surrogates of physical …

This article presents a simple yet powerful new approach for approximating the value of
American options by simulation. The key to this approach is the use of least squares to …

Monte Carlo is one of the most versatile and widely used numerical methods. Its
convergence rate, O (N− 1/2), is independent of dimension, which shows Monte Carlo to be …

Polynomial chaos (PC) expansions are used in stochastic finite element analysis to
represent the random model response by a set of coefficients in a suitable (so-called …