Abstract

The collaboration between computation, theory, and experiment has been a game-changer for academia. This work considers computational physics as an integrative discipline across the natural sciences and utilizes a narrative literature review organized with a conceptual and methodological synthesis. Using peer-reviewed literature from physics, chemistry, biology, and environmental science, as well as science education, the work identifies common interdisciplinary numerical approaches, computational techniques, and algorithms used in modeling and simulation. The research demonstrates that while computational practices in research and education have evolved separately across various fields, the core techniques that have been and continue to be most important for modeling across many fields are those rooted in computational physics: finite difference, finite element, and finite volume methods. Furthermore, the synthesis demonstrates that the combination of modeling in physics and the use of machine learning and other data-driven methods, as well as the importance of computational thinking, are essential for interdisciplinary science and science education. Model formulation, discretization, numerical approximation, algorithm implementation, and data visualization are core components of a generalized computational modeling framework. While this study has provided a unified conceptual framework for multiple academic domains and interdisciplinary curriculum development, its reliance on previously established literature, coupled with the lack of primary empirical findings or simulations, is a notable limitation. This study frames the discipline of computational physics as a methodology, rather than as a field of study with clear boundaries.

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