Detailed and pedagogical account of computational statistical physics, this book covers both theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level.
Detailed and pedagogical account of computational statistical physics, this book covers both theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level.
Lucas Böttcher is Assistant Professor of Computational Social Science at Frankfurt School of Finance and Management and Research Scientist at UCLA's Department of Computational Medicine. His research areas include statistical physics, applied mathematics, complex systems science, and computational physics. He is interested in the application of concepts and models from statistical physics to other disciplines, including biology, ecology, and sociology.
Inhaltsangabe
Preface Part I. Stochastic Methods: 1. Random Numbers 2. Random-Geometrical Models 3. Equilibrium Systems 4. Monte-Carlo Methods 5. Phase Transitions 6. Cluster Algorithms 7. Histogram Methods 8. Renormalization Group 9. Learning and Optimizing 10. Parallelization 11. Non-Equilibrium Systems Part II. Molecular Dynamics: 12. Basic Molecular Dynamics 13. Optimizing Molecular Dynamics 14. Dynamics of Composed Particles 15. Long-Range Potentials 16. Canonical Ensemble 17. Inelastic Collisions in Molecular Dynamics 18. Event-Driven Molecular Dynamics 19. Non-Spherical Particles 20. Contact Dynamics 21. Discrete Fluid Models 22. Ab-Initio Simulations References Index.
