Neural Network Application

Neural network applications are rapidly advancing across diverse scientific domains, driven by the need for efficient and accurate solutions to complex problems. Current research focuses on developing specialized architectures like graph neural networks for irregular data structures (e.g., in mechanics), and adapting existing models (e.g., variational autoencoders and convolutional neural networks) for specific data types such as brain signals (EEG/MEG) and medical images. These efforts aim to improve model interpretability, reduce computational complexity, and enhance performance in areas like medical diagnosis, robotics, and signal processing, ultimately impacting both scientific understanding and practical applications.