Stochastic Differential Equations for Science and Engineering

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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.

Introduction. Section I. Fundamentals. 2. Diffusive Transport and Random Walks. 3. Stochastic Experiments and Probability Spaces. 4. Brownian Motion. 5. Linear Dynamic Systems. Section II Stochastic Calculus. 6. Stochastic Integrals. 7. The Stochastic Chain Rule. 8. Existence, Uniqueness, And Numerics. 9. The Kolmogorov Equations. Section III. Applications. 10. State Estimation. 11. Expectations to The Future. 12. Stochastic Stability Theory. 13. Dynamic Optimization. 14. Perspectives.