# Generative Adversarial Networks in Business and Social Science
## 3 Statistics on GAN Publications
### Knowledge Areas and GANs  
Table 3 has been obtained by taking into account the research areas collected by the WOS and reveals the most relevant research areas (areas that comprise at least 1% of all publications). Regarding the areas that feature a significant number of scientific publications for these GANs, the field of Computer Science stands out and encompasses a wide variety of applications in image processing, deep learning, and synthetic content generation [21; 28; 34; 63; 81]. The field of Engineering presents multiple applications, ranging from the optimisation of renewable energies to manufacturing and monitoring of structural health, which showcases its versatility and utility in solving real-world problems [82; 83; 84; 85]. Of particular interest is the application to cognitive cars [86; 87; 88] and electronic home appliances [89]. Another field where numerous applications are found is in Imaging Science and Photographic Technology. This field is undergoing continuous evolution, for which GANs are contributing significant advancements [25; 90; 91; 92; 93; 94; 95]. It is a major challenge to enumerate the multitude of applications and problems addressed in this field that have been successfully resolved using GANs. Work in Telecommunications also stands out [96; 97; 98; 99; 100]. In the field of Radiology, Nuclear Medicine and Medical Imaging, GANs appear particularly relevant regarding the processing and analysis of medical images by improving their quality and usefulness, aiding in the diagnosis and treatment of diseases, and facilitating advancements in research [102; 103; 104; 105; 106; 107]. Figure 2: Number of articles published per year. Of great significance is the field of Remote Sensing, where GANs have contributed towards enhancing the quality and quantity of data, thereby enabling the interpretation and analysis of images, and introducing new applications and techniques for monitoring and understanding our planet from space [108; 109; 110; 111; 112]. GANs have overcome limitations in the field of Optics related to resolution and quality in the reconstruction of diffracted images, thereby enhancing the capacity for observation and analysis [113; 114; 115]. In the field of Physics, GANs have contributed towards significant advances in simulation techniques, data analysis, and modelling across various domains, including computational physics, particle physics, and quantum physics [116; 117; 118; 119; 120; 121]. In Chemistry, GANs have contributed towards advances in molecular techniques, drug discovery, organic synthesis, and the prediction of chemical properties, thereby opening doors for chemical and pharmaceutical research [122; 123; 124; 125]. In the field of Instruments and Instrumentation, one can find studies applying GANs in deepfake detection, synthetic data generation, instrument calibration, and improvements in resolution in microscopy techniques [126; 127; 128; 129]. In Automatic Control Systems, GANs have contributed innovative solutions in areas such as anomaly detection, system modelling, and synthetic data generation for the design and evaluation of control algorithms [130; 131; 132; 133; 134; 135]. In Materials Science, GANs provide innovative solutions in areas such as material discovery, molecular design, structure optimisation, and the generation of compounds with specific properties [136; 137; 138; 139]. In Geology, several significant applications are related to facies classification techniques, geological pattern generation, image enhancement, and detection of geological features [140; 141; 142; 143]. In Mathematics, innovative solutions have been achieved in areas such as robust optimisation, graph generation, geometric modelling, image compression, and solving