Computational Physics

Fourth edition of a classic on computational physics with new chapters on data science, machine learning and general relativity, plus a plethora of supplementary materials.

Preface
 
PART I. BASICS
Introduction
Software Basics
Errors & Uncertainties
Monte Carlo Simulations
Differentiation & Integration
Trial-and-Error Searching & Data Fitting
Matrix Computing and N-D Searching
Differential Equations & Nonlinear Oscillations
 
PART II. DATA SCIENCE
Fourier Analyses
Wavelet & Principal Components Analysis
Neural Networks & Machine Learning
Quantum Computing
 
PART III. APPLICATIONS
ODE Applications; Eigenvalues, Scattering, Trajectories
Fractals & Statistical Growth Models
Nonlinear Population Dynamics
Nonlinear Dynamics of Continuous Systems
Thermodynamics Simulations & Fenyman Path Integrals
Molecular Dynamics Simulations
General Relativity
Integral Equations
 
PART IV. PDE APPLICATIONS
PDE Review, Electrostatics & Relaxation
Heat Flow & Leapfrogging
String & Membrane Waves
Quantum Wave Packets & EM Waves
Shock & Soliton Waves
Fluid Hydrodynamics
Finite Element Electrostatics
 
Appendices
Index

• By (author) Rubin H. Landau
• By (author) Manuel J. Páez
• By (author) Cristian C. Bordeianu