42 Lectures + 14 Tutorials
Outcome of the Course
This course provides training in computation tools required in research across a wide variety of fields including condensed matter, high energy phenomenology and lattice field theories.
- Introduction to theory of computation and Random numbers.
- Monte Carlo: Importance sampling, Markov chain, Metropolis algorithm, Ising Model and other applications.
- Molecular Dynamics: Integration methods (e.g Verlet and Leap frog algorithms), extended ensembles, molecular system.
- Variational methods for Schrodinger Equation, Hartree and Hartee-Fock methods.
- Quantum Monte Carlo methods.
- Special Topics Like: QMD, Ideal fluids, matrix inversions, Numerical solution of Poisson’s equation: Finite difference method. Particle-Mesh Methods, radiative transfer etc.
- Computational Physics by Joseph Marie Thijssen, Cambridge University Press
- An Introduction to Computational Physics by Tao Pang, Cambridge University press
- Computer Simulation of Liquid by M. P. Allen and D. J. Tildesley, Clarendon press
- A Guide to Monte Carlo Simulations in Statistical Physics by L. Landau and K. Binder
- Quantum Monte Carlo Methods by M. Suzuki (Editor) Springer-Verlag
- New Methods in Computational Quantum Mechanics by I. Prigogine and Stuart A. Rice
- Understanding Molecular Simulation by D. Frankel and B. Smit, Second edition, academic press.
- Computational Methods in Field Theory by H. Gausterer and C.B. Lang (Lecture notes in physics 409)
- Density Functional Theory of Atoms and Molecules by R. G. Parr and W. Yang
- F. Jensen, introduction to Computational Chemistry by F. Jensen
- Essentials of Computational Chemistry by C. J. Crammer
- Dynamical mean field theory by Jean-Marc Robin
- Quantum Monte Carlo Methods by James Gubernatis, Naoki Kawashima, Philipp Werner
- Computer Simulations using Particles - R. W. Hockney and J. W. Eastwood