A Survey of Physics-Informed Neural Networks in Vision Generation

다국어 초록 (Multilingual Abstract)

The field of computer vision has seen remarkable advancements in the domain of generative tasks. These advancements have facilitated the creation of diverse images from complex input data. However, traditional data-driven generative models often fall short in terms of robustness and interpretability, particularly when faced with the challenges of high-dimensional image data and noisy or insufficient training datasets. These limitations are especially problematic in tasks requiring the simulation of physical phenomena, as these models typically generate outputs that may not be physically plausible.
Physics-Informed Neural Networks (PINNs) have emerged as a potent solution to these deficiencies, integrating physical laws directly into the learning process to enhance both the accuracy and generalizability of model predictions. This paper explores the application of PINNs in various computer vision generation tasks, highlighting their utility in generating visually plausible content that adheres to realistic physical constraints. This review explores how incorporating physical laws into GANs and DDPMs, as illustrated in various research studies, addresses the shortcomings of traditional generative models, facilitating more dependable and physically accurate visual simulations across a range of applications. The merging of physics and machine learning in these instances not only stabilizes the training processes but also enhances the fidelity and robustness of generated images. Such insights underscore the broad potential of physics-informed methodologies in advancing computational vision systems, showing that these approaches are instrumental in refining the capabilities of generative models.