mlsanityData to 14 Oct 2026

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

arXiv 2505.06502 ↗First posted 10 May 2025eess.IV cs.CE cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 56 references that are in the map. First posted on arXiv 10 May 2025.

Builds on

15 of 56 references.

References · 15

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3 more
  1. 2021Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusioncited by 38
  2. 2023SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learningcited by 18
  3. 2023Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equationscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modelingcited by 0
  2. 2026Omega: Operator-based Mixture Ensemble for Generative Assimilationcited by 0