Stochastic Methods for Modeling and Predicting Complex Dynamical Systems: Uncertainty Quantification, State Estimation, and Reduced-Order Models
暫譯: 複雜動態系統建模與預測的隨機方法：不確定性量化、狀態估計與降階模型
Chen, Nan
- 出版商: Springer
- 出版日期: 2025-04-13
- 售價: $1,850
- 貴賓價: 9.5 折 $1,758
- 語言: 英文
- 頁數: 225
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031819233
- ISBN-13: 9783031819230
海外代購書籍(需單獨結帳)
商品描述
This Second Edition is an essential guide to understanding, modeling, and predicting complex dynamical systems using new methods with stochastic tools. Expanding upon the original book, the author covers a unique combination of qualitative and quantitative modeling skills, novel efficient computational methods, rigorous mathematical theory, as well as physical intuitions and thinking. The author presents mathematical tools for understanding, modeling, and predicting complex dynamical systems using various suitable stochastic tools. The book provides practical examples and motivations when introducing these tools, merging mathematics, statistics, information theory, computational science, and data science. The author emphasizes the balance between computational efficiency and modeling accuracy while equipping readers with the skills to choose and apply stochastic tools to a wide range of disciplines. This second edition includes updated discussion of combining stochastic models with machine learning and addresses several additional topics, including importance sampling, regression, and maximum likelihood estimate. The author also introduces a new chapter on optimal control.
商品描述(中文翻譯)
本書第二版是理解、建模和預測複雜動態系統的重要指南，使用新方法和隨機工具。作者在原書的基礎上擴展，涵蓋了定性和定量建模技能的獨特組合、新穎的高效計算方法、嚴謹的數學理論，以及物理直覺和思維。作者提供了數學工具，以便使用各種合適的隨機工具來理解、建模和預測複雜的動態系統。書中在介紹這些工具時提供了實際範例和動機，融合了數學、統計學、信息理論、計算科學和數據科學。作者強調計算效率與建模準確性之間的平衡，同時使讀者具備選擇和應用隨機工具於廣泛學科的技能。本第二版包括了有關將隨機模型與機器學習結合的更新討論，並涉及幾個額外主題，包括重要性抽樣、回歸和最大似然估計。作者還介紹了一個有關最優控制的新章節。
作者簡介
Nan Chen, Ph.D., is an Associate Professor at the Department of Mathematics, University of Wisconsin-Madison. He is also a faculty affiliate of the Institute for Foundations of Data Science. Dr. Chen received his Ph.D. from the Courant Institute of Mathematical Sciences and the Center of Atmosphere and Ocean Science, New York University (NYU), in 2016. He worked as a postdoc research associate at NYU for two years before joining UW-Madison. Dr. Chen's research interests lie in applied mathematics, geophysics, complex dynamical systems, stochastic methods, numerical algorithms, and general data science. He is also active in developing dynamical and stochastic models and using these models to analyze and predict real-world phenomena related to atmosphere-ocean science, climate, and other complex systems with the help of real observational data. He has received several awards, including the Kurt O. Friedrichs Prize for an outstanding dissertation in mathematics and the Young Investigator Award from the Office of Naval Research.
作者簡介(中文翻譯)
南陳博士（Nan Chen, Ph.D.）是威斯康辛大學麥迪遜分校數學系的副教授，同時也是數據科學基礎研究所的教職員夥伴。陳博士於2016年在紐約大學（NYU）的Courant數學科學研究所及大氣與海洋科學中心獲得博士學位。在加入威斯康辛大學麥迪遜分校之前，他在紐約大學擔任了兩年的博士後研究助理。陳博士的研究興趣包括應用數學、地球物理學、複雜動態系統、隨機方法、數值算法以及一般數據科學。他也積極開發動態和隨機模型，並利用這些模型分析和預測與大氣-海洋科學、氣候及其他複雜系統相關的現實現象，並結合實際觀測數據進行研究。他曾獲得多項獎項，包括數學領域傑出論文的Kurt O. Friedrichs獎和海軍研究辦公室的青年研究者獎。
