LEADER 04549nam 22007335 450 001 9910997096203321 005 20250606081756.0 010 $a9783031819247 010 $a3031819241 024 7 $a10.1007/978-3-031-81924-7 035 $a(MiAaPQ)EBC32007856 035 $a(Au-PeEL)EBL32007856 035 $a(CKB)38418459700041 035 $a(DE-He213)978-3-031-81924-7 035 $a(EXLCZ)9938418459700041 100 $a20250412d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStochastic Methods for Modeling and Predicting Complex Dynamical Systems $eUncertainty Quantification, State Estimation, and Reduced-Order Models /$fby Nan Chen 205 $a2nd ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (369 pages) 225 1 $aSynthesis Lectures on Mathematics & Statistics,$x1938-1751 311 08$a9783031819230 311 08$a3031819233 327 $aStochastic Toolkits -- Introduction to Information Theory -- Basic Stochastic Computational Methods -- Simple Gaussian and Non-Gaussian SDEs -- Data Assimilation -- Optimal Control -- Prediction -- Data-Driven Low-Order Stochastic Models -- Conditional Gaussian Nonlinear Systems -- Parameter Estimation with Uncertainty Quantification -- Combining Stochastic Models with Machine Learning -- Instruction Manual for the MATLAB Codes. 330 $aThis second edition is an essential guide to understanding, modeling, and predicting complex dynamical systems using new methods with stochastic tools. Expanding upon the original book, the author covers a unique combination of qualitative and quantitative modeling skills, novel efficient computational methods, rigorous mathematical theory, as well as physical intuitions and thinking. The author presents mathematical tools for understanding, modeling, and predicting complex dynamical systems using various suitable stochastic tools. The book provides practical examples and motivations when introducing these tools, merging mathematics, statistics, information theory, computational science, and data science. The author emphasizes the balance between computational efficiency and modeling accuracy while equipping readers with the skills to choose and apply stochastic tools to a wide range of disciplines. This second edition includes updated discussion of combining stochastic models with machine learning and addresses several additional topics, including importance sampling, regression, and maximum likelihood estimate. The author also introduces a new chapter on optimal control. In addition, this book: Covers key topics in modeling and prediction, such as extreme events, high-dimensional systems, and multiscale features Discusses applications for various disciplines including math, physics, engineering, neural science, and ocean science Includes MATLABŪ codes for the provided examples to help readers better understand and apply the concepts About the Author Nan Chen, Ph.D., is an Associate Professor at the Department of Mathematics, University of Wisconsin-Madison. He is also a faculty affiliate of the Institute for Foundations of Data Science. 410 0$aSynthesis Lectures on Mathematics & Statistics,$x1938-1751 606 $aStochastic processes 606 $aStochastic models 606 $aSystem theory 606 $aMathematics 606 $aArtificial intelligence$xData processing 606 $aComputer science 606 $aStochastic Systems and Control 606 $aStochastic Modelling 606 $aComplex Systems 606 $aApplications of Mathematics 606 $aData Science 606 $aModels of Computation 615 0$aStochastic processes. 615 0$aStochastic models. 615 0$aSystem theory. 615 0$aMathematics. 615 0$aArtificial intelligence$xData processing. 615 0$aComputer science. 615 14$aStochastic Systems and Control. 615 24$aStochastic Modelling. 615 24$aComplex Systems. 615 24$aApplications of Mathematics. 615 24$aData Science. 615 24$aModels of Computation. 676 $a515.39 700 $aChen$b Nan$01790421 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910997096203321 996 $aStochastic Methods for Modeling and Predicting Complex Dynamical Systems$94326889 997 $aUNINA