A Review of Computational Physics Methods

Abstract

Computational physics has emerged as a cornerstone of modern scientific research, complementing theoretical analysis and experimental investigation. By employing numerical algorithms, simulations, and high-performance computing, computational physics enables the study of complex physical systems that are analytically intractable or experimentally inaccessible. This review presents a comprehensive examination of computational physics methods, focusing on numerical techniques, simulation approaches, and algorithmic frameworks widely used across physics domains. Key methods such as finite difference, finite element, Monte Carlo, molecular dynamics, and density functional theory are reviewed, along with their applications in classical, quantum, statistical, and astrophysical systems. A comparative analysis highlights strengths, limitations, and computational trade-offs. The paper also discusses emerging trends, including machine learning, exascale computing, and multi-physics simulations, underscoring the growing role of computational physics in advancing scientific discovery.