Machine Learning-Based Surrogate Models for Fluid Dynamics in Computational Physics

Abstract

Computational Fluid Dynamics (CFD) has become one of the most powerful tools in computational physics for studying complex fluid flow phenomena in engineering and scientific applications. However, high-fidelity CFD simulations require substantial computational resources and long execution times, particularly for turbulent, multiphase, and nonlinear flow systems. Machine learning-based surrogate models have emerged as an efficient alternative for accelerating numerical simulations while preserving acceptable prediction accuracy. Surrogate models approximate the behavior of expensive CFD simulations by learning the relationship between input parameters and output responses from existing datasets. These models significantly reduce computational cost and enable rapid optimization, uncertainty quantification, and real-time prediction. In recent years, machine learning techniques such as Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forests (RF), and Deep Learning models have been integrated with CFD workflows to improve predictive capabilities. This paper presents a comprehensive review and discussion of machine learning-based surrogate models for fluid dynamics applications in computational physics. The paper explains surrogate modeling concepts, CFD governing equations, machine learning architectures, data generation strategies, optimization procedures, validation techniques, and performance analysis. Furthermore, challenges, limitations, and future research directions are discussed. The study demonstrates that surrogate modeling techniques provide efficient and reliable solutions for accelerating CFD-based engineering analysis and optimization.