|
Showing 1 - 3 of
3 matches in All Departments
Fully updated with the latest developments in the eigenvalue Monte
Carlo calculations and automatic variance reduction techniques and
containing an entirely new chapter on fission matrix and
alternative hybrid techniques. This second edition explores the
uses of the Monte Carlo method for real-world applications,
explaining its concepts and limitations. Featuring illustrative
examples, mathematical derivations, computer algorithms, and
homework problems, it is an ideal textbook and practical guide for
nuclear engineers and scientists looking into the applications of
the Monte Carlo method, in addition to students in physics and
engineering, and those engaged in the advancement of the Monte
Carlo methods. Describes general and particle-transport-specific
automated variance reduction techniques Presents Monte Carlo
particle transport eigenvalue issues and methodologies to address
these issues Presents detailed derivation of existing and advanced
formulations and algorithms with real-world examples from the
author's research activities
Fully updated with the latest developments in the eigenvalue Monte
Carlo calculations and automatic variance reduction techniques and
containing an entirely new chapter on fission matrix and
alternative hybrid techniques. This second edition explores the
uses of the Monte Carlo method for real-world applications,
explaining its concepts and limitations. Featuring illustrative
examples, mathematical derivations, computer algorithms, and
homework problems, it is an ideal textbook and practical guide for
nuclear engineers and scientists looking into the applications of
the Monte Carlo method, in addition to students in physics and
engineering, and those engaged in the advancement of the Monte
Carlo methods. Describes general and particle-transport-specific
automated variance reduction techniques Presents Monte Carlo
particle transport eigenvalue issues and methodologies to address
these issues Presents detailed derivation of existing and advanced
formulations and algorithms with real-world examples from the
author's research activities
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step
Breakdown with Data, Algorithms, Codes, and Applications delivers a
critical training and resource tool to help engineers understand
machine learning theory and practice, specifically referencing use
cases in oil and gas. The reference moves from explaining how
Python works to step-by-step examples of utilization in various oil
and gas scenarios, such as well testing, shale reservoirs and
production optimization. Petroleum engineers are quickly applying
machine learning techniques to their data challenges, but there is
a lack of references beyond the math or heavy theory of machine
learning. Machine Learning Guide for Oil and Gas Using Python
details the open-source tool Python by explaining how it works at
an introductory level then bridging into how to apply the
algorithms into different oil and gas scenarios. While similar
resources are often too mathematical, this book balances theory
with applications, including use cases that help solve different
oil and gas data challenges.
|
|