Overview
- Highlights the connection between the theory of neutron transport and the theory of non-local branching processes
- Provides readers an entry point into several active areas, including applications related to general radiation transport
- Collects cutting-edge research in the area, serving as a convenient reference
Part of the book series: Probability and Its Applications (PA)
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About this book
This monograph highlights the connection between the theory of neutron transport and the theory of non-local branching processes. By detailing this frequently overlooked relationship, the authors provide readers an entry point into several active areas, particularly applications related to general radiation transport. Cutting-edge research published in recent years is collected here for convenient reference. Organized into two parts, the first offers a modern perspective on the relationship between the neutron branching process (NBP) and the neutron transport equation (NTE), as well as some of the core results concerning the growth and spread of mass of the NBP. The second part generalizes some of the theory put forward in the first, offering proofs in a broader context in order to show why NBPs are as malleable as they appear to be. Stochastic Neutron Transport will be a valuable resource for probabilists, and may also be of interest to numerical analysts and engineersin the field of nuclear research.
Keywords
- Branching processes
- Stochastic neutron transport
- Boltzman transport equation
- Neutron transport equations
- Non-local branching processes
- Classical neutron transport theory
- General radiation transport
- Markov processes
- Branching Markov processes
- Neutron random walk
- Neutron fission modeling
- Monte Carlo simulation
- Martingales
Table of contents (12 chapters)
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Front Matter
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Stochastic Neutron Transport
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Front Matter
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Non-local Branching Markov Processes
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
Emma Horton completed her PhD in 2019 at the University of Bath, where she also completed her undergraduate and masters studies. Following her PhD, she became a postdoc at the IECL, Université de Lorraine. Thereafter, she became chargée de recherche with the project-team ASTRAL, INRIA. She spent over six months as a visiting researcher to the University of Melbourne in 2023 and is currently an Assistant Professor at the University of Warwick, Department of Statistics.
Andreas E. Kyprianou was educated at the University of Oxford and University of Sheffield and is currently a professor of probability theory at the University of Warwick. He has spent almost 30 years working on the theory and application of path-discontinuous stochastic processes and has over 130 publications, including three graduate textbooks. Before moving to Warwick, Andreas spent a large portion of his career at the University of Bath, Department of Mathematical Sciences. Prior to that, he heldvarious positions at the University of Edinburgh, Heriot Watt University, The London School of Economics, as well as working for nearly two years in the oil industry.
Bibliographic Information
Book Title: Stochastic Neutron Transport
Book Subtitle: And Non-Local Branching Markov Processes
Authors: Emma Horton, Andreas E. Kyprianou
Series Title: Probability and Its Applications
DOI: https://doi.org/10.1007/978-3-031-39546-8
Publisher: Birkhäuser Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-39545-1Published: 16 November 2023
Softcover ISBN: 978-3-031-39548-2Due: 29 November 2024
eBook ISBN: 978-3-031-39546-8Published: 15 November 2023
Series ISSN: 2297-0371
Series E-ISSN: 2297-0398
Edition Number: 1
Number of Pages: XV, 272
Number of Illustrations: 6 b/w illustrations, 4 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Statistics and Computing/Statistics Programs, Probability Theory and Stochastic Processes, Statistics and Computing/Statistics Programs