PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918122409164800 |
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| author | Hasan, Md Rakibul Behnoudfar, Pouria MacKinlay, Dan Poulet, Thomas |
| author_facet | Hasan, Md Rakibul Behnoudfar, Pouria MacKinlay, Dan Poulet, Thomas |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_06502 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Hasan, Md Rakibul Behnoudfar, Pouria MacKinlay, Dan Poulet, Thomas Image and Video Processing Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning 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. |
| title | PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations |
| topic | Image and Video Processing Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.06502 |