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

TL;DR

PC-SRGAN is a novel super-resolution GAN that ensures physical consistency in images, significantly improving scientific simulation accuracy with limited data and supporting reliable, interpretable scientific machine learning applications.

Contribution

It introduces PC-SRGAN, a physically consistent super-resolution GAN that enhances image quality while maintaining physical interpretability for scientific simulations.

Findings

Improves Peak Signal-to-Noise Ratio and Structural Similarity Index.

Achieves similar performance with only 13% of training data.

Supports physically meaningful and reliable scientific modeling.

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.