Modeling in Life Sciences and Ecology
Machine Learning & Dynamic System
Series: Springer Asia Pacific Mathematics Series; 4;
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Product details:
- Publisher Springer Nature Singapore
- Date of Publication 19 March 2026
- Number of Volumes 1 pieces, Book
- ISBN 9789819510375
- Binding Hardback
- No. of pages210 pages
- Size 235x155 mm
- Language English
- Illustrations X, 210 p. 73 illus., 62 illus. in color. Illustrations, black & white 0
Categories
Long description:
"
This book begins by exploring the fundamental concepts of dynamical systems and machine learning modeling, elucidating the workflow of these two modeling approaches. While primarily tailored as an introductory textbook for both undergraduate and graduate students, its broader aim is to captivate the interest of seasoned ecologists and life scientists, beckoning them to explore the realm of modeling. The introduction and development of each section adhere to a practical problem-driven approach, aiming to address real-world issues. The focus is on addressing how to establish and evolve appropriate models based on practical problems or data.
Throughout the book, the authors deliver rich content and diverse models. A detailed overview of the workflow for both machine learning and dynamical system modeling is provided, covering topics such as stability and bifurcation theory, fundamentals of machine learning algorithms, data processing, and visualization methods. Regarding dynamical systems, the authors encompass various types of models, including delay, diffusion, continuous, and discrete models. For machine learning, both black-box and interpretable models are covered in this book, including neural network model, ensemble learning model, SHAP, LIME, and more.
Ecologists, life scientists, and applied mathematicians might find this book helpful. It can be also used as a textbook for both undergraduate and graduate students.
This book is related to SDG 15: Life on Land
" MoreTable of Contents:
- 1. Introduction to Dynamical Systems.- 2. Introduce of Machine Learning.- 3. Ecological Modeling with Nonlocal Delay.- 4. Physiological Modeling with Dynamic Systems.- 5. Machine Learning in Clinical Medicine.- 6. Machine Learning in Drug discovery.- 7. Machine Learning in Ecology.
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