Gaussian Integrals and Their Applications

Gaussian Integrals form an integral part of many subfields of applied mathematics and physics, especially in topics such as probability theory, statistics, statistical mechanics, quantum mechanics and so on. They are essential in computing quantities such as the statistical properties of normal random variables, solving partial differential equations involving diffusion processes, and gaining insight into the properties of particles.

In Gaussian Integrals and their Applications, the author has condensed the material deemed essential for undergraduate and graduate students of physics and mathematics, such that for those who are very keen would know what to look for next if their appetite for knowledge remains unsatisfied by the time they finish reading this book.

Features

• A concise and easily digestible treatment of the essentials of Gaussian Integrals
• Suitable for advanced undergraduates and graduate students in mathematics, physics, and statistics
• The only prerequisites are a strong understanding of multivariable calculus and linear algebra.
• Supplemented by numerous exercises (with fully worked solutions) at the end, which pertain to various levels of difficulty and are inspired by different fields in which Gaussian integrals are used.

Oscar A. Nieves is a theoretical physicist working in applied superconductivity research. His research focuses on the modelling of
superconducting electronics, quantum sensors and photonic devices using semi-classical methods.
Oscar completed his PhD in Theoretical Physics from the University of Technology Sydney (UTS) in Australia, with the thesis entitled ＂Noise Dynamics in Stimulated Brillouin Scattering＂. Here, he investigated the role of thermal noise and laser noise in the interaction between light pulses and acoustic waves in dielectric waveguides, otherwise known as the stimulated Brillouin scattering interaction (SBS); and the possible limitations this has for the realization of photonic devices that exploit this interaction, including: amplifiers, filters and short-term memory storage devices. During this degree, Oscar studied stochastic processes very closely, and frequently came across things like the normal and log-normal probability distributions, from which his interest in Gaussian integrals emerged.
Prior to his PhD, he conducted research in simulating the optical response of one-dimensional periodic metasurfaces, investigating the relation between surface curvature and reflectance properties using the Finite-Difference Frequency-Domain (FDFD) method, which he programmed in MATLAB.
His main areas of interest include: numerical methods for physics applications, numerical solution of partial differential equations,
stochastic modelling, quantum devices, electromagnetic theory and machine learning. He also enjoys writing Python codes for solving different types of problems, especially in relation to stochastic dynamics.
Aside from his day-to-day research activities, Oscar also finds happiness in teaching applied mathematics and physics. He has taught a variety of university courses ranging from introductory Calculus and statistics to partial differential equations and complex analysis.