Far Field

Far-field research encompasses the study of phenomena originating from distant sources, addressing challenges in accurately modeling and predicting their effects. Current research focuses on improving the accuracy of far-field predictions across diverse applications, employing techniques like convolutional neural networks, generative adversarial networks, and physics-based models to account for complex environmental factors and varying bathymetry. These advancements are crucial for enhancing the precision of applications ranging from underwater acoustic monitoring and robotic navigation to improved wireless communication systems and advanced radio astronomy.