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Publish a Book Chapter in "Advances in Mathematics for Modeling, Computation and Data Science (Volume - 1)"

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978-1-80433-871-1

Mathematics Edited Book | Edited Book on Mathematics


This edited book on mathematics titled "Advances in Mathematics for Modeling, Computation and Data Science" mainly focuses on various topics such as mathematical modeling basics, differential equation models, dynamical systems etc., and the rest are given below in the Scope of the book. This mathematics edited book will be published with ISBN numbers after following a proper double blind peer reviewed process. All the chapters of this mathematics edited book will be published in a proper style, so that reader can easily understand and learn.

Author can download this mathematics edited book titled "Advances in Mathematics for Modeling, Computation and Data Science" authorship responsibility and copyright form: Click Here

Indexed In


Indexed in Crossref Indexed in Dimensions Indexed in Bowker
ISBN978-1-80433-871-1

Invited Topics

  1. Mathematical Foundations of Modeling in Data-Driven Science
  2. Differential Equations as Core Tools for Predictive Modeling
  3. Stability and Bifurcation Analysis in Nonlinear Dynamical Systems
  4. Stochastic Differential Equations for Uncertain and Noisy Systems
  5. Markov Chains and Hidden Markov Models in Real-World Data
  6. Queueing Theory Models for Service Systems and Network Traffic
  7. Agent-Based and Hybrid Modeling With Mathematical Structure
  8. Optimization Fundamentals for Modern Computation and Analytics
  9. Convex Optimization Theory and Algorithms for Large-Scale Problems
  10. Proximal Methods and Splitting Algorithms in High-Dimensional Optimization
  11. Nonlinear Optimization Techniques for Machine Learning Objectives
  12. Integer and Combinatorial Optimization in Scheduling and Allocation
  13. Variational Methods and Their Role in Data Science Models
  14. Lagrangian Duality and Sensitivity Analysis in Optimization
  15. Graph Theory Foundations for Complex Network Modeling
  16. Spectral Graph Theory Methods for Community Detection
  17. Graph Neural Networks Through the Lens of Mathematics
  18. Network Flow Models and Efficient Algorithms
  19. Numerical Linear Algebra for Data and Scientific Computing
  20. Matrix Decompositions for Feature Extraction and Compression



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ISBN

ISBN: 978-1-80433-871-1

Book Scope

  • Mathematical Modeling Basics
  • Differential Equation Models
  • Dynamical Systems
  • Stochastic Modeling
  • Markov Chains
  • Queueing Models
  • Optimization Methods
  • Convex Optimization
  • Nonlinear Optimization
  • Integer Programming
  • Graph Theory Tools
  • Network Modeling
  • Spectral Graph Methods
  • Numerical Linear Algebra
  • Matrix Factorization
  • Sparse Matrices
  • Iterative Solvers
  • Numerical Integration
  • Numerical PDEs
  • Finite Difference Methods
  • Finite Element Methods
  • Spectral Methods
  • Monte Carlo Methods
  • Bayesian Computation
  • Time Series Models
  • Stochastic Processes
  • Random Matrix Theory
  • Probability Bounds
  • Statistical Learning Theory
  • Regression Methods
  • Classification Methods
  • Clustering Algorithms
  • Dimensionality Reduction
  • PCA and SVD
  • Kernel Methods
  • Gaussian Processes
  • Neural Network Theory
  • Deep Learning Optimization
  • Gradient Methods
  • Automatic Differentiation
  • Computational Geometry
  • Topological Data Analysis
  • Information Theory Metrics
  • Entropy and Divergence
  • Signal Processing Models
  • Compressed Sensing
  • Scientific Computing
  • High-Performance Computing
  • Model Validation
  • Uncertainty Quantification


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Deadline

31 Jan 2026