Source Target Domain

Source-target domain mapping focuses on learning effective transformations between different data domains, enabling applications like transferring knowledge from large datasets to smaller ones or adapting models to new environments. Current research emphasizes developing neural operator architectures, such as Fourier Neural Operators and their variants, to learn these mappings directly from data, addressing challenges like unequal-domain sizes and boundary-to-domain predictions. This research is crucial for improving the generalizability and efficiency of machine learning models across diverse applications, ranging from scientific simulations and image generation to robotics and medical imaging. The development of robust and efficient domain adaptation techniques is key to unlocking the full potential of deep learning in real-world scenarios.