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arXiv · 2505.06502

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations

Md Rakibul Hasan·Pouria Behnoudfar·Dan MacKinlay·Thomas Poulet
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Abstract

Machine Learning, particularly Generative Adversarial Networks (GANs), has revolutionised Super-Resolution (SR). However, generated images often lack physical meaningfulness, which is essential for scientific applications. Our approach, PC-SRGAN, enhances image resolution while ensuring physical consistency for interpretable simulations. PC-SRGAN significantly improves both the Peak Signal-to-Noise Ratio and the Structural Similarity Index Measure compared to conventional SR methods, even with limited training data (e.g., only 13% of training data is required to achieve performance similar to SRGAN). Beyond SR, PC-SRGAN augments physically meaningful machine learning, incorporating numerically justified time integrators and advanced quality metrics. These advancements promise reliable and causal machine-learning models in scientific domains. A significant advantage of PC-SRGAN over conventional SR techniques is its physical consistency, which makes it a viable surrogate model for time-dependent problems. PC-SRGAN advances scientific machine learning by improving accuracy and efficiency, enhancing process understanding, and broadening applications to scientific research. We publicly release the complete source code of PC-SRGAN and all experiments at https://github.com/hasan-rakibul/PC-SRGAN.

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Md Rakibul Hasan, Pouria Behnoudfar, Dan MacKinlay, Thomas Poulet. 2025-08-12. PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations. https://doi.org/10.1109/tpami.2025.3596647

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