Pere Colet - Stochastic Numerical Methods : An Introduction for Students and Scientists read online EPUB, PDF, FB2
9783527411498 English 3527411496 Stochastic Numerical Methods introduces at Master level the numerical methods that use probability or stochastic concepts to analyze random processes. The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where some of the techniques have been used recently to numerically simulate different agent-based models. Examples included in the book range from phase-transitions and critical phenomena, including details of data analysis (extraction of critical exponents, finite-size effects, etc.), to population dynamics, interfacial growth, chemical reactions, etc. Program listings are integrated in the discussion of numerical algorithms to facilitate their understanding. From the contents: Review of Probability Concepts Monte Carlo Integration Generation of Uniform and Non-uniform Random Numbers: Non-correlated Values Dynamical Methods Applications to Statistical Mechanics Introduction to Stochastic Processes Numerical Simulation of Ordinary and Partial Stochastic Differential Equations Introduction to Master Equations Numerical Simulations of Master Equations Hybrid Monte Carlo Generation of n-Dimensional Correlated Gaussian Variables Collective Algorithms for Spin Systems Histogram Extrapolation Multicanonical Simulations, The book introduces at a masters level the numerical methods that use probability or stochastic concepts to analyze random processes. The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where some of the techniques have been used recently to numerically simulate different agent-based models. The authors develop in detail examples from the phase-transitions field to explain the whole process from the numerical simulation (design of the convenient algorithm) to the data analysis (extraction of critical exponents, finite-size effects, etc). The core of the book covers Monte Carlo type methods with applications to statistical physics and phase transitions, numerical methods for stochastic differential equations - both ordinary and partial (including advanced pseudo-spectral methods-, Gillespies method to simulate the dynamics of systems described by master equations (e.g. birth and death processes, and applications to Biology, such as protein expression and transcription). Finally, and in order to explain modern hybrid algorithms (combining Monte Carlo and stochastic differential equations), the authors explain the basics of molecular dynamics. Appendices with supplementary material for more advanced topics, end-of-chapter practical exercises, and useful codes for the core methods are included.
9783527411498 English 3527411496 Stochastic Numerical Methods introduces at Master level the numerical methods that use probability or stochastic concepts to analyze random processes. The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where some of the techniques have been used recently to numerically simulate different agent-based models. Examples included in the book range from phase-transitions and critical phenomena, including details of data analysis (extraction of critical exponents, finite-size effects, etc.), to population dynamics, interfacial growth, chemical reactions, etc. Program listings are integrated in the discussion of numerical algorithms to facilitate their understanding. From the contents: Review of Probability Concepts Monte Carlo Integration Generation of Uniform and Non-uniform Random Numbers: Non-correlated Values Dynamical Methods Applications to Statistical Mechanics Introduction to Stochastic Processes Numerical Simulation of Ordinary and Partial Stochastic Differential Equations Introduction to Master Equations Numerical Simulations of Master Equations Hybrid Monte Carlo Generation of n-Dimensional Correlated Gaussian Variables Collective Algorithms for Spin Systems Histogram Extrapolation Multicanonical Simulations, The book introduces at a masters level the numerical methods that use probability or stochastic concepts to analyze random processes. The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where some of the techniques have been used recently to numerically simulate different agent-based models. The authors develop in detail examples from the phase-transitions field to explain the whole process from the numerical simulation (design of the convenient algorithm) to the data analysis (extraction of critical exponents, finite-size effects, etc). The core of the book covers Monte Carlo type methods with applications to statistical physics and phase transitions, numerical methods for stochastic differential equations - both ordinary and partial (including advanced pseudo-spectral methods-, Gillespies method to simulate the dynamics of systems described by master equations (e.g. birth and death processes, and applications to Biology, such as protein expression and transcription). Finally, and in order to explain modern hybrid algorithms (combining Monte Carlo and stochastic differential equations), the authors explain the basics of molecular dynamics. Appendices with supplementary material for more advanced topics, end-of-chapter practical exercises, and useful codes for the core methods are included.