Deterministic and Stochastic Modeling in Computational Electromagnetics: Integral and Differential Equation Approaches

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Deterministic computational models are those for which all inputs are precisely known, whereas stochastic modeling reflects uncertainty or randomness in one or more of the data inputs. Many problems in computational engineering therefore require both deterministic and stochastic modeling to be used in parallel, allowing for different degrees of confidence and incorporating datasets of different kinds. In particular, non-intrusive stochastic methods can be easily combined with widely-used deterministic approaches, enabling this more robust form of data analysis to be applied to a range of computational challenges.

Deterministic and Stochastic Modeling in Computational Electromagnetics provides a rare treatment of parallel deterministic-stochastic computational modeling and its beneficial applications. Unlike other works of its kind, which generally treat deterministic and stochastic modeling in isolation from one another, it aims to demonstrate the usefulness of a combined approach and present particular use-cases in which such an approach is clearly required. It offers a non-intrusive stochastic approach which can be incorporated with minimal effort into virtually all existing computational models.

Readers will also find:

• A range of specific examples demonstrating the efficiency of deterministic-stochastic modeling
• Computational examples of successful applications including ground penetrating radars (GPR), radiation from 5G systems, transcranial magnetic and electric stimulation (TMS and TES), and more
• Introduction to fundamental principles in field theory to ground the discussion of computational modeling

Deterministic and Stochastic Modeling in Computational Electromagnetics is a valuable reference for researchers, including graduate and undergraduate students, in computational electromagnetics, as well as to multidisciplinary researchers, engineers, physicists, and mathematicians.

這本重要的資料安全參考書將幫助您保護網絡。確定性計算模型是指所有輸入都是精確已知的，而隨機建模則反映了一個或多個數據輸入中的不確定性或隨機性。因此，許多計算工程問題需要同時使用確定性和隨機建模，以允許不同程度的信心並結合不同類型的數據集。特別是，非侵入性隨機方法可以輕鬆地與廣泛使用的確定性方法結合，從而使這種更強大的數據分析形式應用於各種計算挑戰中。

《確定性和隨機建模在計算電磁學中的應用》提供了對並行確定性-隨機計算建模及其有益應用的罕見探討。與其他類似的作品不同，該書旨在展示結合方法的有用性並呈現明顯需要此方法的特定用例。它提供了一種非侵入性的隨機方法，可以輕鬆地與幾乎所有現有的計算模型相結合。

讀者還將找到：
- 一系列具體示例，展示了確定性-隨機建模的效率
- 成功應用的計算示例，包括地下探測雷達（GPR）、5G系統的輻射、頭部磁場和電場刺激（TMS和TES）等
- 引入場論的基本原理，以鞏固對計算建模的討論

《確定性和隨機建模在計算電磁學中的應用》對計算電磁學的研究人員（包括研究生和本科生）以及多學科研究人員、工程師、物理學家和數學家都是一本有價值的參考書。

Dragan Poljak, Ph.D, is Professor in the Department of Electronics and Computing Technology, University of Split, Croatia. He is a Senior Member of the IEEE and author of three books and more than one hunderd and fifty articles on subjects related to computational electromagnetics.

Anna Susnjara, Ph.D, is a Postdoctoral Researcher in the Department of Electronics and Computing Technology, University of Split, Croatia. She is a member of the IEEE and has authored or co-authored more than 40 journal and conference papers on subjects related to computational electromagnetics.

Dragan Poljak, Ph.D.，是克羅地亞斯普利特大學電子與計算技術系的教授。他是IEEE的高級會員，並且是三本書和超過150篇與計算電磁學相關的文章的作者。

Anna Susnjara, Ph.D.，是克羅地亞斯普利特大學電子與計算技術系的博士後研究員。她是IEEE的成員，並且在計算電磁學相關的期刊和會議上撰寫或合著了40多篇論文。