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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

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Indexed in Crossref Indexed in Dimensions Indexed in Bowker
Invited Topics

  1. Sparse Linear Systems and Iterative Solver Strategies
  2. Randomized Numerical Linear Algebra for Big Data Applications
  3. Numerical Integration and Quadrature Methods for High Accuracy
  4. Finite Difference Methods for PDE-Based Modeling
  5. Finite Element Methods for Multiphysics Simulations
  6. Spectral Methods for Efficient PDE Computation
  7. Adaptive Mesh Refinement and Error Estimation Techniques
  8. Monte Carlo Simulation and Variance Reduction Methods
  9. Quasi-Monte Carlo and Low-Discrepancy Sequences in Computation
  10. Bayesian Computation Using MCMC and Variational Inference
  11. Probabilistic Modeling and Bayesian Decision Theory
  12. Time Series Modeling: Stationarity, Trends and Seasonality
  13. State-Space Models and Kalman Filtering for Sequential Data
  14. Stochastic Processes and Applications in Finance and Engineering
  15. Random Matrix Theory in High-Dimensional Statistics
  16. Concentration Inequalities and Generalization Bounds in Learning
  17. Statistical Learning Theory and Risk Minimization Principles
  18. Regularization Methods for Robust Regression
  19. Robust Statistics and Outlier-Resistant Modeling
  20. Classification Theory: Margin, Loss Functions and Consistency
  21. Clustering Methods and Objective-Based Formulations
  22. Dimensionality Reduction Using PCA, SVD and Manifold Learning
  23. Kernel Methods and Reproducing Kernel Hilbert Spaces
  24. Support Vector Machines and Convex Learning Formulations
  25. Gaussian Processes for Regression and Uncertainty Estimation
  26. Neural Network Approximation Theory and Expressivity
  27. Optimization Landscapes in Deep Learning: Theory and Practice
  28. Gradient Descent, Acceleration and Adaptive Optimization Methods
  29. Automatic Differentiation and Computational Graph Mathematics
  30. Numerical Stability and Conditioning in Data-Intensive Computation
  31. Compressed Sensing and Sparse Recovery Theory
  32. Signal Processing Models Through Linear Operators and Transforms
  33. Wavelets and Multiresolution Analysis for Data Representation
  34. Information Theory Measures for Machine Learning and Inference
  35. Entropy, Divergence and Mutual Information in Feature Selection
  36. Optimal Transport Theory and Computational Algorithms
  37. Topological Data Analysis: Persistent Homology and Applications
  38. Computational Geometry for Data Science and Visualization
  39. Voronoi Diagrams and Delaunay Triangulations in Modeling
  40. Approximation Theory for Function Learning and Interpolation
  41. Polynomial Approximation and Chebyshev Methods in Computation
  42. Numerical Optimization for PDE-Constrained Learning Problems
  43. Inverse Problems and Regularization in Imaging and Sensing
  44. Uncertainty Quantification for Mathematical Models and Predictions
  45. Sensitivity Analysis and Parameter Identifiability in Models
  46. Model Calibration Techniques Using Optimization and Bayesian Methods
  47. Validation Metrics and Goodness-of-Fit for Mathematical Models
  48. Surrogate Modeling and Reduced-Order Modeling Strategies
  49. Scientific Machine Learning and Physics-Informed Neural Networks
  50. Operator Learning and Neural PDE Solvers: Mathematical Viewpoints
  51. Multiscale Modeling and Homogenization Techniques
  52. Computational Methods for Complex Systems and Emergent Behavior
  53. High-Performance Computing for Large-Scale Mathematical Models
  54. Parallel Algorithms for Linear Algebra and Optimization
  55. Numerical Methods for Big Data: Streaming and Online Computation
  56. Randomized Algorithms in Data Science and Numerical Analysis
  57. Graph Algorithms for Large-Scale Networks and Social Data
  58. Optimization for Recommender Systems and Ranking Models
  59. Mathematical Methods for Anomaly Detection and Rare Event Modeling
  60. Change-Point Detection and Sequential Hypothesis Testing
  61. Mathematical Foundations of Reinforcement Learning and Control
  62. Dynamic Programming, Bellman Equations and Approximate Solutions
  63. Game Theory Models for Multi-Agent Data Systems
  64. Fairness Constraints and Mathematical Formulations in ML
  65. Privacy and Differential Privacy: Mathematical Guarantees
  66. Causal Inference: Graphical Models and Identification Theory
  67. Experimental Design and Active Learning with Mathematical Criteria
  68. Statistical Resampling: Bootstrap Methods and Confidence Intervals
  69. Large Deviations Theory and Extreme Value Modeling
  70. Risk Measures and Robust Optimization Under Uncertainty
  71. Numerical Methods for Graphical Models and Probabilistic Inference
  72. Matrix Completion and Low-Rank Recovery for Missing Data
  73. Tensor Methods for Multidimensional Data and Factorization
  74. Optimization and Mathematics of Deep Generative Models
  75. Mathematical Approaches to Explainability and Interpretability
  76. Benchmarking Mathematical Models: Reproducibility and Best Practices
  77. Emerging Trends in Mathematical Methods for Data Science
  78. Interdisciplinary Case Studies in Modeling and Computation
  79. Translating Mathematical Theory into Scalable Data Systems
  80. Roadmap for Future Research in Mathematical Modeling and Data Science
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