Stochastic Differential Equations for Science and Engineering

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A01=Uffe Hogsbro Thygesen
advanced stochastic process applications
Advective Time Scale
Algebraic Riccati Equation
Author_Uffe Hogsbro Thygesen
Backward Kolmogorov Equation
Brownian Motion
Category=PBKJ
Category=PBWL
Cir Process
Conditional Expectation
Cox Ingersoll Ross Process
Differential Lyapunov Equation
Dynamical system
Dynkin's Formula
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Euler Maruyama Method
Euler Maruyama Scheme
Geometric Brownian Motion
graduate applied mathematics
HJB Equation
Lamperti Transform
Linear Stochastic Differential Equations
Markov chains
Mathematics for Engineering
numerical simulation methods
Ordinary Differential Equation
Ornstein Uhlenbeck Process
Probability
Quadratic Variation
R programming algorithms
random walk analysis
Sample Paths
stability in control systems
Standard Brownian Motion
Statistics
Stochastic Calculus
Stochastic Differential Equations
stochastic modeling techniques
Stratonovich Integral
Stratonovich Stochastic Differential Equation
Van Der Pol Oscillator

Product details

  • ISBN 9781032232171
  • Weight: 535g
  • Dimensions: 156 x 234mm
  • Publication Date: 15 Jun 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Stochastic Differential Equations for Science and Engineering is aimed at students at the M.Sc. and PhD level. The book describes the mathematical construction of stochastic differential equations with a level of detail suitable to the audience, while also discussing applications to estimation, stability analysis, and control. The book includes numerous examples and challenging exercises. Computational aspects are central to the approach taken in the book, so the text is accompanied by a repository on GitHub containing a toolbox in R which implements algorithms described in the book, code that regenerates all figures, and solutions to exercises.

Features:

  • Contains numerous exercises, examples, and applications
  • Suitable for science and engineering students at Master’s or PhD level
  • Thorough treatment of the mathematical theory combined with an accessible treatment of motivating examples
  • GitHub repository available at: https://github.com/Uffe-H-Thygesen/SDEbook and https://github.com/Uffe-H-Thygesen/SDEtools

Uffe Høgsbro Thygesen received his Ph.D. degree from the Technical University of Denmark in 1999, based on a thesis on stochastic control theory. He worked with applications to marine ecology and fisheries until 2017, where he joined the Department of Applied Mathematics and Computer Science at the same university. His research interests are centered on deterministic and stochastic dynamic systems and involve times series analysis, control, and dynamic games, primarily with applications in life science. In his spare time he teaches sailing and kayaking and learns guitar and photography.

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