Real-Time Heat Transfer Simulation Using a Surrogate Model Based on Generative Adversarial Networks (GANs)

Abstract

Thermal system heat transfer simulation is essential in many fields, like electronics, aerospace, and energy. In real-time applications, such as interactive design, digital twins, or online control systems, traditional techniques like Finite Element Method (FEM) or Computational Fluid Dynamics (CFD) are both accurate and expensive, requiring extensive computation. These methods are precisely why we propose a thermal phenomenon surrogate modeling framework utilizing Generative Adversarial Networks (GANs). Our approach utilizes Conditional GAN (GCN) architecture where Generators output temperature fields based on the specified input parameters, pre-defined boundaries, conditions, material tech characteristics, and geometry. The Discriminator Network improves the fidelity and realism of the retrieved data by identifying whether the model-derived outputs are truly surpassing high-fidelity simulated outputs—model verification. We assembled a detailed dataset from high-res numerical simulations to train and validate the surrogate model. Our findings proved that a GAN-based surrogate model achieves spend value MSE and SSIM while outperforming conventional solvers by orders of magnitude, generating millisecond results, and maintaining high accuracy relative to ground truth simulations. Further, the model's robustness to unseen boundary conditions and geometric configurations enhances its versatility for various thermal analysis scenarios. This research underscores the transformative possibilities for real-time simulation in computational physics that deep generative models can provide. The proposed surrogate model strikes a viable compromise between efficiency and accuracy, expanding its scope for practical deployment in real-time thermal monitoring, design cycle optimization, and physics-based AI frameworks.